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Li, Yanbin, Yue Li, Huanliang Xu, and Shougang Ren. "An Adaptive Communication-Efficient Federated Learning to Resist Gradient-Based Reconstruction Attacks." Security and Communication Networks 2021 (April 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/9919030.
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The widely deployed devices in Internet of Things (IoT) have opened up a large amount of IoT data. Recently, federated learning emerges as a promising solution aiming to protect user privacy on IoT devices by training a globally shared model. However, the devices in the complex IoT environments pose great challenge to federate learning, which is vulnerable to gradient-based reconstruction attacks. In this paper, we discuss the relationships between the security of federated learning model and optimization technologies of decreasing communication overhead comprehensively. To promote the efficiency and security, we propose a defence strategy of federated learning which is suitable to resource-constrained IoT devices. The adaptive communication strategy is to adjust the frequency and parameter compression by analysing the training loss to ensure the security of the model. The experiments show the efficiency of our proposed method to decrease communication overhead, while preventing privacy data leakage.
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Chen, Mingzhe, Nir Shlezinger, H. Vincent Poor, Yonina C. Eldar, and Shuguang Cui. "Communication-efficient federated learning." Proceedings of the National Academy of Sciences 118, no. 17 (April 22, 2021): e2024789118. http://dx.doi.org/10.1073/pnas.2024789118.
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Federated learning (FL) enables edge devices, such as Internet of Things devices (e.g., sensors), servers, and institutions (e.g., hospitals), to collaboratively train a machine learning (ML) model without sharing their private data. FL requires devices to exchange their ML parameters iteratively, and thus the time it requires to jointly learn a reliable model depends not only on the number of training steps but also on the ML parameter transmission time per step. In practice, FL parameter transmissions are often carried out by a multitude of participating devices over resource-limited communication networks, for example, wireless networks with limited bandwidth and power. Therefore, the repeated FL parameter transmission from edge devices induces a notable delay, which can be larger than the ML model training time by orders of magnitude. Hence, communication delay constitutes a major bottleneck in FL. Here, a communication-efficient FL framework is proposed to jointly improve the FL convergence time and the training loss. In this framework, a probabilistic device selection scheme is designed such that the devices that can significantly improve the convergence speed and training loss have higher probabilities of being selected for ML model transmission. To further reduce the FL convergence time, a quantization method is proposed to reduce the volume of the model parameters exchanged among devices, and an efficient wireless resource allocation scheme is developed. Simulation results show that the proposed FL framework can improve the identification accuracy and convergence time by up to 3.6% and 87% compared to standard FL.
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Balakrishnan, Ravikumar, Mustafa Akdeniz, Sagar Dhakal, Arjun Anand, Ariela Zeira, and Nageen Himayat. "Resource Management and Model Personalization for Federated Learning over Wireless Edge Networks." Journal of Sensor and Actuator Networks 10, no. 1 (February 23, 2021): 17. http://dx.doi.org/10.3390/jsan10010017.
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Client and Internet of Things devices are increasingly equipped with the ability to sense, process, and communicate data with high efficiency. This is resulting in a major shift in machine learning (ML) computation at the network edge. Distributed learning approaches such as federated learning that move ML training to end devices have emerged, promising lower latency and bandwidth costs and enhanced privacy of end users’ data. However, new challenges that arise from the heterogeneous nature of the devices’ communication rates, compute capabilities, and the limited observability of the training data at each device must be addressed. All these factors can significantly affect the training performance in terms of overall accuracy, model fairness, and convergence time. We present compute-communication and data importance-aware resource management schemes optimizing these metrics and evaluate the training performance on benchmark datasets. We also develop a federated meta-learning solution, based on task similarity, that serves as a sample efficient initialization for federated learning, as well as improves model personalization and generalization across non-IID (independent, identically distributed) data. We present experimental results on benchmark federated learning datasets to highlight the performance gains of the proposed methods in comparison to the well-known federated averaging algorithm and its variants.
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Lim, Hyun-Kyo, Ju-Bong Kim, Joo-Seong Heo, and Youn-Hee Han. "Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices." Sensors 20, no. 5 (March 2, 2020): 1359. http://dx.doi.org/10.3390/s20051359.
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Reinforcement learning has recently been studied in various fields and also used to optimally control IoT devices supporting the expansion of Internet connection beyond the usual standard devices. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type but with slightly different dynamics. For such multiple IoT devices, there is no guarantee that an agent who interacts only with one IoT device and learns the optimal control policy will also control another IoT device well. Therefore, we may need to apply independent reinforcement learning to each IoT device individually, which requires a costly or time-consuming effort. To solve this problem, we propose a new federated reinforcement learning architecture where each agent working on its independent IoT device shares their learning experience (i.e., the gradient of loss function) with each other, and transfers a mature policy model parameters into other agents. They accelerate its learning process by using mature parameters. We incorporate the actor–critic proximal policy optimization (Actor–Critic PPO) algorithm into each agent in the proposed collaborative architecture and propose an efficient procedure for the gradient sharing and the model transfer. Using multiple rotary inverted pendulum devices interconnected via a network switch, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices and that the learning speed can be faster if more agents are involved.
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Liu, Jessica Chia, Jack Goetz, Srijan Sen, and Ambuj Tewari. "Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data." JMIR mHealth and uHealth 9, no. 3 (March 30, 2021): e23728. http://dx.doi.org/10.2196/23728.
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Background The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. Objective We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. Methods We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. Results In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. Conclusions Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.
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Bellavista, Paolo, Luca Foschini, and Alessio Mora. "Decentralised Learning in Federated Deployment Environments." ACM Computing Surveys 54, no. 1 (April 2021): 1–38. http://dx.doi.org/10.1145/3429252.
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Decentralised learning is attracting more and more interest because it embodies the principles of data minimisation and focused data collection, while favouring the transparency of purpose specification (i.e., the objective for which a model is built). Cloud-centric-only processing and deep learning are no longer strict necessities to train high-fidelity models; edge devices can actively participate in the decentralised learning process by exchanging meta-level information in place of raw data, thus paving the way for better privacy guarantees. In addition, these new possibilities can relieve the network backbone from unnecessary data transfer and allow it to meet strict low-latency requirements by leveraging on-device model inference. This survey provides a detailed and up-to-date overview of the most recent contributions available in the state-of-the-art decentralised learning literature. In particular, it originally provides the reader audience with a clear presentation of the peculiarities of federated settings, with a novel taxonomy of decentralised learning approaches, and with a detailed description of the most relevant and specific system-level contributions of the surveyed solutions for privacy, communication efficiency, non-IIDness, device heterogeneity, and poisoning defense.
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Sun, Jianguo, Zining Yan, and Sizhao Li. "Multiagent Minimum Risk Path Intrusion Strategy with Computational Geometry." Wireless Communications and Mobile Computing 2021 (July 8, 2021): 1–18. http://dx.doi.org/10.1155/2021/9974279.
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In wireless sensor networks (WSNs), inefficient coverage does affect the quality of service (QoS), which the minimum exposure path (MEP) is traditionally used to handle. But intelligent mobile devices are generally of limited computation capability, local storage, and energy. Present methods cannot meet the demand of multiple target intrusion, lacking the consideration of energy consumption. Based on the Voronoi diagram in computational geometry, this paper proposed an invasion strategy of minimum risk path (MRP) to such a question. MRP is the path considered both the exposure of the moving target and energy consumption. Federated learning is introduced to figure out how to find the MRP, expressed as C t i , t j = f E , e . The value of C t i , t j can measure the success of an invasion. At the time when a single smart mobile device invades, horizontal federated learning is taken to partition the path feature, and a single target feature federated (SPF) algorithm is for calculating the MRP. Moreover, for multi smart mobile device invasion, it has imported the time variable. Vertical federated learning can partition the feature of multipath data, and the multi-target feature federated (MFF) algorithm is for solving the multipath MRP dynamically. The experimental results show that the SPF and MFF have the dominant advantage over traditional computational performance and time. It primarily applies the complex conditions of a massive amount of sensor nodes.
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Ito, Rei, Mineto Tsukada, and Hiroki Matsutani. "An On-Device Federated Learning Approach for Cooperative Model Update Between Edge Devices." IEEE Access 9 (2021): 92986–98. http://dx.doi.org/10.1109/access.2021.3093382.
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De Vita, Fabrizio, and Dario Bruneo. "Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems." Journal of Sensor and Actuator Networks 9, no. 4 (December 18, 2020): 59. http://dx.doi.org/10.3390/jsan9040059.
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During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy.
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Lin, Tzu-Wei, and Chien-Lung Hsu. "FAIDM for Medical Privacy Protection in 5G Telemedicine Systems." Applied Sciences 11, no. 3 (January 27, 2021): 1155. http://dx.doi.org/10.3390/app11031155.
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5G networks have an efficient effect in energy consumption and provide a quality experience to many communication devices. Device-to-device communication is one of the key technologies of 5G networks. Internet of Things (IoT) applying 5G infrastructure changes the application scenario in many fields especially real-time communication between machines, data, and people. The 5G network has expanded rapidly around the world including in healthcare. Telemedicine provides long-distance medical communication and services. Patient can get help with ambulatory care or other medical services in remote areas. 5G and IoT will become important parts of next generation smart medical healthcare. Telemedicine is a technology of electronic message and telecommunication related to healthcare, which is implemented in public networks. Privacy issue of transmitted information in telemedicine is important because the information is sensitive and private. In this paper, 5G-based federated anonymous identity management for medical privacy protection is proposed, and it can provide a secure way to protect medical privacy. There are some properties below. (i) The proposed scheme provides federated identity management which can manage identity of devices in a hierarchical structure efficiently. (ii) Identity authentication will be achieved by mutual authentication. (iii) The proposed scheme provides session key to secure transmitted data which is related to privacy of patients. (iv) The proposed scheme provides anonymous identities for devices in order to reduce the possibility of leaking transmitted medical data and real information of device and its owner. (v) If one of devices transmit abnormal data, proposed scheme provides traceability for servers of medical institute. (vi) Proposed scheme provides signature for non-repudiation.
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Jeon, Joohyung, Soohyun Park, Minseok Choi, Joongheon Kim, Young-Bin Kwon, and Sungrae Cho. "Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms." Electronics 9, no. 9 (August 21, 2020): 1359. http://dx.doi.org/10.3390/electronics9091359.
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Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of mobile edge computing. In this paper, we propose a novel control algorithm for high-performance and stabilized queue in FEEL system. We consider that the FEEL environment includes the clients transmit data to associated federated edges; these edges then locally update the global model, which is downloaded from the central server via a backhaul. Obtaining greater quantities of local data from the clients facilitates more accurate global model construction; however, this may be harmful in terms of queue stability in the edge, owing to substantial data arrivals from the clients. Therefore, the proposed algorithm varies the number of clients selected for transmission, with the aim of maximizing the time-averaged federated learning accuracy subject to queue stability. Based on this number of clients, the federated edge selects the clients to transmit on the basis of resource status.
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Thorgeirsson, Adam Thor, and Frank Gauterin. "Probabilistic Predictions with Federated Learning." Entropy 23, no. 1 (December 30, 2020): 41. http://dx.doi.org/10.3390/e23010041.
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Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting.
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Fan, Tongrang, Jian Liu, and Feng Gao. "Dynamic Pricing Strategy of Shared Devices in IIU Federated Cloud." International Journal of Control and Automation 9, no. 2 (February 28, 2016): 199–210. http://dx.doi.org/10.14257/ijca.2016.9.2.19.
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Kholod, Ivan, Evgeny Yanaki, Dmitry Fomichev, Evgeniy Shalugin, Evgenia Novikova, Evgeny Filippov, and Mats Nordlund. "Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis." Sensors 21, no. 1 (December 29, 2020): 167. http://dx.doi.org/10.3390/s21010167.
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The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis are not always applicable due to the large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. FL is being actively developed, and currently, there are several open-source frameworks that implement it. This article presents a comparative review and analysis of the existing open-source FL frameworks, including their applicability in IoT systems. The authors evaluated the following features of the frameworks: ease of use and deployment, development, analysis capabilities, accuracy, and performance. Three different data sets were used in the experiments—two signal data sets of different volumes and one image data set. To model low-power IoT devices, computing nodes with small resources were defined in the testbed. The research results revealed FL frameworks that could be applied in the IoT systems now, but with certain restrictions on their use.
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Huang, Anbu, Yang Liu, Tianjian Chen, Yongkai Zhou, Quan Sun, Hongfeng Chai, and Qiang Yang. "StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing." ACM Transactions on Intelligent Systems and Technology 12, no. 4 (August 2021): 1–23. http://dx.doi.org/10.1145/3467956.
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From facial recognition to autonomous driving, Artificial Intelligence (AI) will transform the way we live and work over the next couple of decades. Existing AI approaches for urban computing suffer from various challenges, including dealing with synchronization and processing of vast amount of data generated from the edge devices, as well as the privacy and security of individual users, including their bio-metrics, locations, and itineraries. Traditional centralized-based approaches require data in each organization be uploaded to the central database, which may be prohibited by data protection acts, such as GDPR and CCPA. To decouple model training from the need to store the data in the cloud, a new training paradigm called Federated Learning (FL) is proposed. FL enables multiple devices to collaboratively learn a shared model while keeping the training data on devices locally, which can significantly mitigate privacy leakage risk. However, under urban computing scenarios, data are often communication-heavy, high-frequent, and asynchronized, posing new challenges to FL implementation. To handle these challenges, we propose a new hybrid federated learning architecture called StarFL. By combining with Trusted Execution Environment (TEE), Secure Multi-Party Computation (MPC), and (Beidou) satellites, StarFL enables safe key distribution, encryption, and decryption, and provides a verification mechanism for each participant to ensure the security of the local data. In addition, StarFL can provide accurate timestamp matching to facilitate synchronization of multiple clients. All these improvements make StarFL more applicable to the security-sensitive scenarios for the next generation of urban computing.
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Nasiri, Sara, Iman Nasiri, and Kristof Van Laerhoven. "Wearable xAI: A Knowledge-Based Federated Learning Framework." Engineering Proceedings 6, no. 1 (May 17, 2021): 79. http://dx.doi.org/10.3390/i3s2021dresden-10143.
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Federated learning is a knowledge transmission and training process that occurs in turn between user models on edge devices and the training model in the central server. Due to privacy policies and concerns and heterogeneous data, this is a widespread requirement in federated learning applications. In this work, we use knowledge-based methods, and in particular case-based reasoning (CBR), to develop a wearable, explainable artificial intelligence (xAI) framework. CBR is a problem-solving AI approach for knowledge representation and manipulation, which considers successful solutions of past conditions that are likely to serve as candidate solutions for a requested problem. It enables federated learning when each user owns not only his/her private data, but also uniquely designed cases. New generated cases can be compared to the knowledge base and the recommendations enable the user to communicate better with the whole system. It improves users’ task performance and increases user acceptability when they need explanations to understand why and how AI algorithms arrive at these optimal solutions.
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Jiang, Ji Chu, Burak Kantarci, Sema Oktug, and Tolga Soyata. "Federated Learning in Smart City Sensing: Challenges and Opportunities." Sensors 20, no. 21 (October 31, 2020): 6230. http://dx.doi.org/10.3390/s20216230.
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Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter.
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Wang, Cong, Yuanyuan Yang, and Pengzhan Zhou. "Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity." IEEE Transactions on Parallel and Distributed Systems 32, no. 2 (February 1, 2021): 394–410. http://dx.doi.org/10.1109/tpds.2020.3023905.
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Savazzi, Stefano, Monica Nicoli, and Vittorio Rampa. "Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks." IEEE Internet of Things Journal 7, no. 5 (May 2020): 4641–54. http://dx.doi.org/10.1109/jiot.2020.2964162.
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Shi, Minyu, Yongting Zhang, Huanhuan Wang, Junfeng Hu, and Xiang Wu. "A Clonal Selection Optimization System for Multiparty Secure Computing." Complexity 2021 (July 9, 2021): 1–14. http://dx.doi.org/10.1155/2021/7638394.
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The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.
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Wu, Xing, Zhaowang Liang, and Jianjia Wang. "FedMed: A Federated Learning Framework for Language Modeling." Sensors 20, no. 14 (July 21, 2020): 4048. http://dx.doi.org/10.3390/s20144048.
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Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next word or phrase and facilitating the human-machine interaction in a virtual keyboard of the smartphone or laptop. Mobile keyboard prediction with FL hopes to satisfy the growing demand that high-level data privacy be preserved in artificial intelligence applications even with the distributed models training. However, there are two major problems in the federated optimization for the prediction: (1) aggregating model parameters on the server-side and (2) reducing communication costs caused by model weights collection. To address the above issues, traditional FL methods simply use averaging aggregation or ignore communication costs. We propose a novel Federated Mediation (FedMed) framework with the adaptive aggregation, mediation incentive scheme, and topK strategy to address the model aggregation and communication costs. The performance is evaluated in terms of perplexity and communication rounds. Experiments are conducted on three datasets (i.e., Penn Treebank, WikiText-2, and Yelp) and the results demonstrate that our FedMed framework achieves robust performance and outperforms baseline approaches.
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Dramé-Maigné, Sophie, Maryline Laurent, Laurent Castillo, and Hervé Ganem. "Centralized, Distributed, and Everything in between." ACM Computing Surveys 54, no. 7 (September 30, 2022): 1–34. http://dx.doi.org/10.1145/3465170.
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The Internet of Things is taking hold in our everyday life. Regrettably, the security of IoT devices is often being overlooked. Among the vast array of security issues plaguing the emerging IoT, we decide to focus on access control, as privacy, trust, and other security properties cannot be achieved without controlled access. This article classifies IoT access control solutions from the literature according to their architecture (e.g., centralized, hierarchical, federated, distributed) and examines the suitability of each one for access control purposes. Our analysis concludes that important properties such as auditability and revocation are missing from many proposals while hierarchical and federated architectures are neglected by the community. Finally, we provide an architecture-based taxonomy and future research directions: a focus on hybrid architectures, usability, flexibility, privacy, and revocation schemes in serverless authorization.
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Xu, Ronghua, Yu Chen, Erik Blasch, and Genshe Chen. "BlendCAC: A Smart Contract Enabled Decentralized Capability-Based Access Control Mechanism for the IoT." Computers 7, no. 3 (July 13, 2018): 39. http://dx.doi.org/10.3390/computers7030039.
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While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today’s AC is the centralized authorization server, which can cause a performance bottleneck or be the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable effective protection for devices, services and information in large-scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI nodes) and more powerful computing devices (i.e., laptops) and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.
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Simon, Denis Vasilyevich, and Irina Sergeevna Shakhova. "Distributed Training of ML Model on Mobile Devices." Russian Digital Libraries Journal 23, no. 5 (August 23, 2020): 1076–92. http://dx.doi.org/10.26907/1562-5419-2020-23-5-1076-1092.
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Currently, the need for distributed ML training solutions in the world is increasing. However, existing tools, in particular TensorFlow Federated, are at the very beginning of their development, difficult to implement, and currently suitable exclusively for simulation on servers. For mobile devices, reliable approaches for this purpose do not exist. This article has designed and presented an approach to such distributed training of the ML-model on mobile devices, implemented on existing technologies. It is based on the concept of model personalization. In this approach, this concept is improved as a consequence of mitigating the identified drawbacks. The implementation process is structured so that at all stages of working with the ML-model use only one Swift programming language (Swift for TensorFlow and Core ML 3 are used), making this approach even more convenient and reliable due to the common code base.
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Bennis, Mehdi. "Federated Learning and Control at the Wireless Network Edge." GetMobile: Mobile Computing and Communications 24, no. 3 (January 22, 2021): 9–13. http://dx.doi.org/10.1145/3447853.3447857.
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We are at the cusp of two transformational technologies, namely the fifth generation of wireless communication systems, known as 5G, and machine learning (ML). On the one hand, while the evolutionary part of 5G, enhanced mobile broadband (eMBB), focusing mainly on millimeter-wave transmissions has made significant progress, fundamentals of ultra-reliable and low-latency communication (URLLC), one of the major tenets of the 5G revolution, are yet to be fully understood. In essence, URLLC warrants a departure from average-based system design toward a clean slate design centered on tail, risk, and scale [1]. While risk is encountered when dealing with decision making under uncertainty, scale is driven by the sheer amount of devices, antennas, sensors, and actuators, all of which pose unprecedented challenges in network design, optimization, and scalability.
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Sater, Raed Abdel, and A. Ben Hamza. "A Federated Learning Approach to Anomaly Detection in Smart Buildings." ACM Transactions on Internet of Things 2, no. 4 (November 30, 2021): 1–23. http://dx.doi.org/10.1145/3467981.
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Internet of Things (IoT) sensors in smart buildings are becoming increasingly ubiquitous, making buildings more livable, energy efficient, and sustainable. These devices sense the environment and generate multivariate temporal data of paramount importance for detecting anomalies and improving the prediction of energy usage in smart buildings. However, detecting these anomalies in centralized systems is often plagued by a huge delay in response time. To overcome this issue, we formulate the anomaly detection problem in a federated learning setting by leveraging the multi-task learning paradigm, which aims at solving multiple tasks simultaneously while taking advantage of the similarities and differences across tasks. We propose a novel privacy-by-design federated learning model using a stacked long short-time memory (LSTM) model, and we demonstrate that it is more than twice as fast during training convergence compared to the centralized LSTM. The effectiveness of our federated learning approach is demonstrated on three real-world datasets generated by the IoT production system at General Electric Current smart building, achieving state-of-the-art performance compared to baseline methods in both classification and regression tasks. Our experimental results demonstrate the effectiveness of the proposed framework in reducing the overall training cost without compromising the prediction performance.
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Celesti, Antonio, Maria Fazio, Antonio Puliafito, and Massimo Villari. "Sensed Data Sharing in Cloud Federation for Advances in Health Information Exchange." International Journal of Measurement Technologies and Instrumentation Engineering 3, no. 4 (October 2013): 36–50. http://dx.doi.org/10.4018/ijmtie.2013100104.
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In this paper the authors focus on sensing systems supporting data exchange among several healthcare administrative domains. The challenge in this area is twofold: efficient management of a huge amount of data produced by medical devices, bio-sensors and information systems, sharing sensed data for scientific and clinical purposes. The authors present a new information system that exploits Cloud computing capabilities to overcome such issues, also guaranteeing patients' privacy. Their proposal integrates different healthcare institutions into a federated environment, thus establishing a trust context among the institutions themselves. The storage service is designed according to a fully distributed approach and it is based on the wide-used Open Source framework Hadoop, which is enriched to establish a compelling federated system. They adopt the XRI technology to formalize an XML-based data model which allows to simplify the classification, searching and retrieval of medical data.
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Kasyap, Harsh, and Somanath Tripathy. "Privacy-preserving Decentralized Learning Framework for Healthcare System." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 2s (June 10, 2021): 1–24. http://dx.doi.org/10.1145/3426474.
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Clinical trials and drug discovery would not be effective without the collaboration of institutions. Earlier, it has been at the cost of individual’s privacy. Several pacts and compliances have been enforced to avoid data breaches. The existing schemes collect the participant’s data to a central repository for learning predictions as the collaboration is indispensable for research advances. The current COVID pandemic has put a question mark on our existing setup where the existing data repository has proved to be obsolete. There is a need for contemporary data collection, processing, and learning. The smartphones and devices held by the last person of the society have also made them a potential contributor. It demands to design a distributed and decentralized Collaborative Learning system that would make the knowledge inference from every data point. Federated Learning [21], proposed by Google, brings the concept of in-place model training by keeping the data intact to the device. Though it is privacy-preserving in nature, however, it is susceptible to inference, poisoning, and Sybil attacks. Blockchain is a decentralized programming paradigm that provides a broader control of the system, making it attack resistant. It poses challenges of high computing power, storage, and latency. These emerging technologies can contribute to the desired learning system and motivate them to address their security and efficiency issues. This article systematizes the security issues in Federated Learning, its corresponding mitigation strategies, and Blockchain’s challenges. Further, a Blockchain-based Federated Learning architecture with two layers of participation is presented, which improves the global model accuracy and guarantees participant’s privacy. It leverages the channel mechanism of Blockchain for parallel model training and distribution. It facilitates establishing decentralized trust between the participants and the gateways using the Blockchain, which helps to have only honest participants.
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Celesti, Antonio, Armando Ruggeri, Maria Fazio, Antonino Galletta, Massimo Villari, and Agata Romano. "Blockchain-Based Healthcare Workflow for Tele-Medical Laboratory in Federated Hospital IoT Clouds." Sensors 20, no. 9 (May 2, 2020): 2590. http://dx.doi.org/10.3390/s20092590.
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In a pandemic situation such as that we are living at the time of writing of this paper due to the Covid-19 virus, the need of tele-healthcare service becomes dramatically fundamental to reduce the movement of patients, thence reducing the risk of infection. Leveraging the recent Cloud computing and Internet of Things (IoT) technologies, this paper aims at proposing a tele-medical laboratory service where clinical exams are performed on patients directly in a hospital by technicians through IoT medical devices and results are automatically sent via the hospital Cloud to doctors of federated hospitals for validation and/or consultation. In particular, we discuss a distributed scenario where nurses, technicians and medical doctors belonging to different hospitals cooperate through their federated hospital Clouds to form a virtual health team able to carry out a healthcare workflow in secure fashion leveraging the intrinsic security features of the Blockchain technology. In particular, both public and hybrid Blockchain scenarios are discussed and assessed using the Ethereum platform.
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Cheng, Xiang, Qian Luo, Ye Pan, Zitong Li, Jiale Zhang, and Bing Chen. "Predicting the APT for Cyber Situation Comprehension in 5G-Enabled IoT Scenarios Based on Differentially Private Federated Learning." Security and Communication Networks 2021 (April 21, 2021): 1–14. http://dx.doi.org/10.1155/2021/8814068.
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Driven by the advancements in 5G-enabled Internet of Things (IoT) technologies, the IoT devices have shown an explosive growth trend with massive data generated at the edge of the network. However, IoT systems exhibit inherent vulnerability for diverse attacks, and Advanced Persistent Threat (APT) is one of the most powerful attack models that could lead to a significant privacy leakage of systems. Moreover, recent detection technologies can hardly meet the demands of effective security defense against APTs. To address the above problems, we propose an APT Prediction Method based on Differentially Private Federated Learning (APTPMFL) to predict the probability of subsequent APT attacks occurring in IoT systems. It is the first time to apply a federated learning mechanism for aggregating suspicious activities in the IoT systems, where the APT prediction phase does not need any correlation rules. Moreover, to achieve privacy-preserving property, we further adopt a differentially private data perturbation mechanism to add the Laplacian random noises to the IoT device training data features, so as to achieve the maximum protection of privacy data. We also present a 5G-enabled edge computing-based framework to train and deploy the model, which can alleviate the computing and communication overhead of the typical IoT systems. Our evaluation results show that APTPMFL can efficiently predict subsequent APT behaviors in the IoT system accurately and efficiently.
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Asad, Muhammad, Ahmed Moustafa, and Chao Yu. "A Critical Evaluation of Privacy and Security Threats in Federated Learning." Sensors 20, no. 24 (December 15, 2020): 7182. http://dx.doi.org/10.3390/s20247182.
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With the advent of smart devices, smartphones, and smart everything, the Internet of Things (IoT) has emerged with an incredible impact on the industries and human life. The IoT consists of millions of clients that exchange massive amounts of critical data, which results in high privacy risks when processed by a centralized cloud server. Motivated by this privacy concern, a new machine learning paradigm has emerged, namely Federated Learning (FL). Specifically, FL allows for each client to train a learning model locally and performs global model aggregation at the centralized cloud server in order to avoid the direct data leakage from clients. However, despite this efficient distributed training technique, an individual’s private information can still be compromised. To this end, in this paper, we investigate the privacy and security threats that can harm the whole execution process of FL. Additionally, we provide practical solutions to overcome those attacks and protect the individual’s privacy. We also present experimental results in order to highlight the discussed issues and possible solutions. We expect that this work will open exciting perspectives for future research in FL.
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Meng, Linhao, Yating Wei, Rusheng Pan, Shuyue Zhou, Jianwei Zhang, and Wei Chen. "VADAF: Visualization for Abnormal Client Detection and Analysis in Federated Learning." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (December 31, 2021): 1–23. http://dx.doi.org/10.1145/3426866.
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Federated Learning (FL) provides a powerful solution to distributed machine learning on a large corpus of decentralized data. It ensures privacy and security by performing computation on devices (which we refer to as clients) based on local data to improve the shared global model. However, the inaccessibility of the data and the invisibility of the computation make it challenging to interpret and analyze the training process, especially to distinguish potential client anomalies. Identifying these anomalies can help experts diagnose and improve FL models. For this reason, we propose a visual analytics system, VADAF, to depict the training dynamics and facilitate analyzing potential client anomalies. Specifically, we design a visualization scheme that supports massive training dynamics in the FL environment. Moreover, we introduce an anomaly detection method to detect potential client anomalies, which are further analyzed based on both the client model’s visual and objective estimation. Three case studies have demonstrated the effectiveness of our system in understanding the FL training process and supporting abnormal client detection and analysis.
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Phea, Sinchhean, Zhishang Wang, Jiangkun Wang, and Abderazek Ben Abdallah. "Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System." SHS Web of Conferences 102 (2021): 04017. http://dx.doi.org/10.1051/shsconf/202110204017.
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Recent years have witnessed a rapid growth of Artificial Intelligence (AI) in biomedical fields. However, an accurate and secure system for pneumonia detection and diagnosis is urgently needed. We present the optimization and implementation of a collaborative learning algorithm for an AI-Enabled Real-time Biomedical System (AIRBiS), where a convolution neural network is deployed for pneumonia (i.e., COVID-19) image classification. With augmentation optimization, the federated learning (FL) approach achieves a high accuracy of 95.66%, which outperforms the conventional learning approach with an accuracy of 94.08%. Using multiple edge devices also reduces overall training time.
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Zhang, Peiying, Hao Sun, Jingyi Situ, Chunxiao Jiang, and Dongliang Xie. "Federated Transfer Learning for IIoT Devices With Low Computing Power Based on Blockchain and Edge Computing." IEEE Access 9 (2021): 98630–38. http://dx.doi.org/10.1109/access.2021.3095078.
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Alam, Mahbub Ul, and Rahim Rahmani. "Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application." Sensors 21, no. 15 (July 24, 2021): 5025. http://dx.doi.org/10.3390/s21155025.
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Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.
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Gálvez, Rafa, Veelasha Moonsamy, and Claudia Diaz. "Less is More: A privacy-respecting Android malware classifier using federated learning." Proceedings on Privacy Enhancing Technologies 2021, no. 4 (July 23, 2021): 96–116. http://dx.doi.org/10.2478/popets-2021-0062.
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Abstract In this paper we present LiM (‘Less is More’), a malware classification framework that leverages Federated Learning to detect and classify malicious apps in a privacy-respecting manner. Information about newly installed apps is kept locally on users’ devices, so that the provider cannot infer which apps were installed by users. At the same time, input from all users is taken into account in the federated learning process and they all benefit from better classification performance. A key challenge of this setting is that users do not have access to the ground truth (i.e. they cannot correctly identify whether an app is malicious). To tackle this, LiM uses a safe semi-supervised ensemble that maximizes classification accuracy with respect to a baseline classifier trained by the service provider (i.e. the cloud). We implement LiM and show that the cloud server has F1 score of 95%, while clients have perfect recall with only 1 false positive in > 100 apps, using a dataset of 25K clean apps and 25K malicious apps, 200 users and 50 rounds of federation. Furthermore, we conduct a security analysis and demonstrate that LiM is robust against both poisoning attacks by adversaries who control half of the clients, and inference attacks performed by an honest-but-curious cloud server. Further experiments with Ma-MaDroid’s dataset confirm resistance against poisoning attacks and a performance improvement due to the federation.
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Javed, Asad, Jérémy Robert, Keijo Heljanko, and Kary Främling. "IoTEF: A Federated Edge-Cloud Architecture for Fault-Tolerant IoT Applications." Journal of Grid Computing 18, no. 1 (January 10, 2020): 57–80. http://dx.doi.org/10.1007/s10723-019-09498-8.
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AbstractThe evolution of Internet of Things (IoT) technology has led to an increased emphasis on edge computing for Cyber-Physical Systems (CPS), in which applications rely on processing data closer to the data sources, and sharing the results across heterogeneous clusters. This has simplified the data exchanges between IoT/CPS systems, the cloud, and the edge for managing low latency, minimal bandwidth, and fault-tolerant applications. Nonetheless, many of these applications administer data collection on the edge and offer data analytic and storage capabilities in the cloud. This raises the problem of separate software stacks between the edge and the cloud with no unified fault-tolerant management, hindering dynamic relocation of data processing. In such systems, the data must also be preserved from being corrupted or duplicated in the case of intermittent long-distance network connectivity issues, malicious harming of edge devices, or other hostile environments. Within this context, the contributions of this paper are threefold: (i) to propose a new Internet of Things Edge-Cloud Federation (IoTEF) architecture for multi-cluster IoT applications by adapting our earlier Cloud and Edge Fault-Tolerant IoT (CEFIoT) layered design. We address the fault tolerance issue by employing the Apache Kafka publish/subscribe platform as the unified data replication solution. We also deploy Kubernetes for fault-tolerant management, combined with the federated scheme, offering a single management interface and allowing automatic reconfiguration of the data processing pipeline, (ii) to formulate functional and non-functional requirements of our proposed solution by comparing several IoT architectures, and (iii) to implement a smart buildings use case of the ongoing Otaniemi3D project as proof-of-concept for assessing IoTEF capabilities. The experimental results conclude that the architecture minimizes latency, saves network bandwidth, and handles both hardware and network connectivity based failures.
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Xiao, Peng, Samuel Cheng, Vladimir Stankovic, and Dejan Vukobratovic. "Averaging Is Probably Not the Optimum Way of Aggregating Parameters in Federated Learning." Entropy 22, no. 3 (March 11, 2020): 314. http://dx.doi.org/10.3390/e22030314.
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Federated learning is a decentralized topology of deep learning, that trains a shared model through data distributed among each client (like mobile phones, wearable devices), in order to ensure data privacy by avoiding raw data exposed in data center (server). After each client computes a new model parameter by stochastic gradient descent (SGD) based on their own local data, these locally-computed parameters will be aggregated to generate an updated global model. Many current state-of-the-art studies aggregate different client-computed parameters by averaging them, but none theoretically explains why averaging parameters is a good approach. In this paper, we treat each client computed parameter as a random vector because of the stochastic properties of SGD, and estimate mutual information between two client computed parameters at different training phases using two methods in two learning tasks. The results confirm the correlation between different clients and show an increasing trend of mutual information with training iteration. However, when we further compute the distance between client computed parameters, we find that parameters are getting more correlated while not getting closer. This phenomenon suggests that averaging parameters may not be the optimum way of aggregating trained parameters.
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Doherty, Cailbhe, Arash Joorabchi, Peter Megyesi, Aileen Flynn, and Brian Caulfield. "Physiotherapists’ Use of Web-Based Information Resources to Fulfill Their Information Needs During a Theoretical Examination: Randomized Crossover Trial." Journal of Medical Internet Research 22, no. 12 (December 17, 2020): e19747. http://dx.doi.org/10.2196/19747.
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Background The widespread availability of internet-connected smart devices in the health care setting has the potential to improve the delivery of research evidence to the care pathway and fulfill health care professionals’ information needs. Objective This study aims to evaluate the frequency with which physiotherapists experience information needs, the capacity of digital information resources to fulfill these needs, and the specific types of resources they use to do so. Methods A total of 38 participants (all practicing physiotherapists; 19 females, 19 males) were randomly assigned to complete three 20-question multiple-choice questionnaire (MCQ) examinations under 3 conditions in a randomized crossover study design: assisted by a web browser, assisted by a federated search portal system, and unassisted. MCQ scores, times, and frequencies of information needs were recorded for overall examination-level and individual question-level analyses. Generalized estimating equations were used to assess differences between conditions for the primary outcomes. A log file analysis was conducted to evaluate participants’ web search and retrieval behaviors. Results Participants experienced an information need in 55.59% (845/1520) MCQs (assisted conditions only) and exhibited a mean improvement of 10% and 16% in overall examination scores for the federated search and web browser conditions, respectively, compared with the unassisted condition (P<.001). In the web browser condition, Google was the most popular resource and the only search engine used, accounting for 1273 (64%) of hits, followed by PubMed (195 hits; 10% of total). In the federated search condition, Wikipedia and PubMed were the most popular resources with 1518 (46% of total) and 1273 (39% of total) hits, respectively. Conclusions In agreement with the findings of previous research studies among medical physicians, the results of this study demonstrate that physiotherapists frequently experience information needs. This study provides new insights into the preferred digital information resources used by physiotherapists to fulfill these needs. Future research should clarify the implications of physiotherapists’ apparent high reliance on Google, whether these results reflect the authentic clinical environment, and whether fulfilling clinical information needs alters practice behaviors or improves patient outcomes.
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Xiao, Tuo, Taiping Cui, S. M. Riazul Islam, and Qianbin Chen. "Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs." Sensors 21, no. 1 (December 31, 2020): 215. http://dx.doi.org/10.3390/s21010215.
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With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture to accommodate the fast growing data traffic and improve the performance of network service. By deploying content caching in F-RAN, fast and repeatable data access can be achieved, which reduces network traffic and transmission latency. Due to the capacity limit of caches, it is essential to predict the popularity of the content and pre-cache them in edge nodes. In general, the classic prediction approaches require the gathering of users’ personal information at a central unit, giving rise to users’ privacy issues. In this paper, we propose an intelligent F-RANs framework based on federated learning (FL), which does not require gathering user data centrally on the server for training, so it can effectively ensure the privacy of users. In the work, federated learning is applied to user demand prediction, which can accurately predict the content popularity distribution in the network. In addition, to minimize the total traffic cost of the network in consideration of user content requests, we address the allocation of storage resources and content placement in the network as an integrated model and formulate it as an Integer Linear Programming (ILP) problem. Due to the high computational complexity of the ILP problem, two heuristic algorithms are designed to solve it. Simulation results show that the performance of our proposed algorithm is close to the optimal solution.
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CHRISTOPHE, BENOIT. "MANAGING MASSIVE DATA OF THE INTERNET OF THINGS THROUGH COOPERATIVE SEMANTIC NODES." International Journal of Semantic Computing 06, no. 04 (December 2012): 389–408. http://dx.doi.org/10.1142/s1793351x12400120.
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The Internet of Things refers to extending the Internet to physical entities of interest (EoI) to humans (e.g., a table, a room or another human being) sensed as a set of properties that can be observed, measured, accessed or triggered by devices such as actuators, sensors or other smart components. In this vision, the IoT foresees novel types of applications dynamically finding the associations between devices and EoIs around a common feature of interest (e.g., temperature of a room) to provide meaningful information as well as rich services to users about the things they are interested in. Growing interest in providing sensors and actuators has led to billions of services or data offered through different platforms, some of them wrapped with semantic descriptions to realize aforementioned associations through accurate search processes. However, due to the ubiquitous aspect of the IoT and the potential mobility of the devices that enable it, a centralized approach does not allow designing scalable processes to efficiently search and manage these associations or the devices and EoIs that compose them. As location seems to be an important parameter when searching the IoT, we believe that designing a framework composed of geographically distributed nodes with local reasoning capabilities is a much more scalable approach to realize the IoT vision. We describe our approach of such a vision by creating a federated network composed of such nodes that declare their location based on a formal model. In this vision, each node is capable of processing semantic descriptions of devices or EoIs to share deduced associations with other peers that are selected based on their location nearness.
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Kohlar, Florian, Jörg Schwenk, Meiko Jensen, and Sebastian Gajek. "On Cryptographically Strong Bindings of SAML Assertions to Transport Layer Security." International Journal of Mobile Computing and Multimedia Communications 3, no. 4 (October 2011): 20–35. http://dx.doi.org/10.4018/jmcmc.2011100102.
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In recent research, two approaches to protect SAML based Federated Identity Management (FIM) against man-in-the-middle attacks have been proposed. One approach is to bind the SAML assertion and the SAML artifact to the public key contained in a TLS client certificate. Another approach is to strengthen the Same Origin Policy of the browser by taking into account the security guarantees TLS gives. This work presents a third approach which is of further interest beyond IDM protocols, especially for mobile devices relying heavily on the security offered by web technologies. By binding the SAML assertion to cryptographically derived values of the TLS session that has been agreed upon between client and the service provider, this approach provides anonymity of the (mobile) browser while allowing Relying Party and Identity Provider to detect the presence of a man-in-the-middle attack.
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Weichhart, Georg, and Michael Rosemann. "Guest Editorial: Cooperative Information Systems in the Digital Age." International Journal of Cooperative Information Systems 25, no. 04 (December 2016): 1702001. http://dx.doi.org/10.1142/s0218843017020014.
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Cooperative information systems (CIS) are at the core of a digital economy that is dedicated to providing hyper-connected citizens with access to intuitive and sophisticated technology. No longer are commercial applications the only driver of development activities, but wider societal and individual deployment areas are now explored. CIS researchers have for more than two decades studied the nature and requirements of cooperative systems linking latest technologies with hybrid user bases. However, the current opportunity-rich landscape of digital technologies such as mobile and social computing, advanced data analytics, robotic process automation or smart devices has triggered entire new, exciting research questions. In light of this setting, the 23rd International Conference on Cooperative Information Systems took place within the context of the 14th OnTheMove Federated Conferences and Workshops in Rhodes, Greece, in October 2015. This Special Issue features the carefully selected and comprehensively reviewed best papers from this event.
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Zhang, Zhizhao, Tianzhi Yang, and Yuan Liu. "SABlockFL: a blockchain-based smart agent system architecture and its application in federated learning." International Journal of Crowd Science 4, no. 2 (May 4, 2020): 133–47. http://dx.doi.org/10.1108/ijcs-12-2019-0037.
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Purpose The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques. Design/methodology/approach In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage. Findings A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design. Originality/value The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.
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Putra, Karisma Trinanda, Hsing-Chung Chen, Prayitno, Marek R. Ogiela, Chao-Lung Chou, Chien-Erh Weng, and Zon-Yin Shae. "Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications." Sensors 21, no. 13 (July 4, 2021): 4586. http://dx.doi.org/10.3390/s21134586.
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The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.
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Sánchez, Luis, Jorge Lanza, Juan Santana, Rachit Agarwal, Pierre Raverdy, Tarek Elsaleh, Yasmin Fathy, et al. "Federation of Internet of Things Testbeds for the Realization of a Semantically-Enabled Multi-Domain Data Marketplace." Sensors 18, no. 10 (October 10, 2018): 3375. http://dx.doi.org/10.3390/s18103375.
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The Internet of Things (IoT) concept has attracted a lot of attention from the research and innovation community for a number of years already. One of the key drivers for this hype towards the IoT is its applicability to a plethora of different application domains. However, infrastructures enabling experimental assessment of IoT solutions are scarce. Being able to test and assess the behavior and the performance of any piece of technology (i.e., protocol, algorithm, application, service, etc.) under real-world circumstances is of utmost importance to increase the acceptance and reduce the time to market of these innovative developments. This paper describes the federation of eleven IoT deployments from heterogeneous application domains (e.g., smart cities, maritime, smart building, crowd-sensing, smart grid, etc.) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day. The paper summarizes the resources that are made available through a cloud-based platform. The main contributions from this paper are twofold. In the one hand, the insightful summary of the federated data resources are relevant to the experimenters that might be seeking for an experimental infrastructure to assess their innovations. On the other hand, the identification of the challenges met during the testbed integration process, as well as the mitigation strategies that have been implemented to face them, are of interest for testbed providers that can be considering to join the federation.
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Karakaya, Aykut, and Sedat Akleylek. "A novel IoT-based health and tactical analysis model with fog computing." PeerJ Computer Science 7 (February 3, 2021): e342. http://dx.doi.org/10.7717/peerj-cs.342.
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In sports competitions, depending on the conditions such as excitement, stress, fatigue, etc. during the match, negative situations such as disability or loss of life may occur for players and spectators. Therefore, it is extremely important to constantly check their health. In addition, some strategic analyzes are made during the match. According to the results of these analyzes, the technical team affects the course of the match. Effects can have positive and sometimes negative results. In this article, fog computing and an Internet of Things (IoT) based architecture are proposed to produce new technical strategies and to avoid disabilities. Players and spectators are monitored with sensors such as blood pressure, body temperature, heart rate, location etc. The data obtained from the sensors are processed in the fog layer and the resulting information is sent to the devices of the technical team and club doctors. In the architecture based on fog computing and IoT, priority processes are computed with low latency. For this, a task management algorithm based on priority queue and list of fog nodes is modified in the fog layer. Authentication and data confidentiality are provided with the Federated Lightweight Authentication of Things (FLAT) method used in the proposed model. In addition, using the Software Defined Network controller based on blockchain technology ensures data integrity.
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Hulsen, Tim. "Sharing Is Caring—Data Sharing Initiatives in Healthcare." International Journal of Environmental Research and Public Health 17, no. 9 (April 27, 2020): 3046. http://dx.doi.org/10.3390/ijerph17093046.
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In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these ‘big data’ put together can be utilized to optimize treatments for each unique patient (‘precision medicine’). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the ‘valley of death’ of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.
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Tatschner, Stefan, Ferdinand Jarisch, Alexander Giehl, Sven Plaga, and Thomas Newe. "The Stream Exchange Protocol: A Secure and Lightweight Tool for Decentralized Connection Establishment." Sensors 21, no. 15 (July 21, 2021): 4969. http://dx.doi.org/10.3390/s21154969.
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With the growing availability and prevalence of internet-capable devices, the complexity of networks and associated connection management increases. Depending on the use case, different approaches in handling connectivity have emerged over the years, tackling diverse challenges in each distinct area. Exposing centralized web-services facilitates reachability; distributing information in a peer-to-peer fashion offers availability; and segregating virtual private sub-networks promotes confidentiality. A common challenge herein lies in connection establishment, particularly in discovering, and securely connecting to peers. However, unifying different aspects, including the usability, scalability, and security of this process in a single framework, remains a challenge. In this paper, we present the Stream Exchange Protocol (SEP) collection, which provides a set of building blocks for secure, lightweight, and decentralized connection establishment. These building blocks use unique identities that enable both the identification and authentication of single communication partners. By utilizing federated directories as decentralized databases, peers are able to reliably share authentic data, such as current network locations and available endpoints. Overall, this collection of building blocks is universally applicable, easy to use, and protected by state-of-the-art security mechanisms by design. We demonstrate the capabilities and versatility of the SEP collection by providing three tools that utilize our building blocks: a decentralized file sharing application, a point-to-point network tunnel using the SEP trust model, and an application that utilizes our decentralized discovery mechanism for authentic and asynchronous data distribution.
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Yousuf, Omerah, and Roohie Naaz Mir. "A survey on the Internet of Things security." Information & Computer Security 27, no. 2 (June 12, 2019): 292–323. http://dx.doi.org/10.1108/ics-07-2018-0084.
Full textAbstract:
Purpose Internet of Things (IoT) is a challenging and promising system concept and requires new types of architectures and protocols compared to traditional networks. Security is an extremely critical issue for IoT that needs to be addressed efficiently. Heterogeneity being an inherent characteristic of IoT gives rise to many security issues that need to be addressed from the perspective of new architectures such as software defined networking, cryptographic algorithms, federated cloud and edge computing. Design/methodology/approach The paper analyzes the IoT security from three perspectives: three-layer security architecture, security issues at each layer and security countermeasures. The paper reviews the current state of the art, protocols and technologies used at each layer of security architecture. The paper focuses on various types of attacks that occur at each layer and provides the various approaches used to countermeasure such type of attacks. Findings The data exchanged between the different devices or applications in the IoT environment are quite sensitive; thus, the security aspect plays a key role and needs to be addressed efficiently. This indicates the urgent needs of developing general security policy and standards for IoT products. The efficient security architecture needs to be imposed but not at the cost of efficiency and scalability. The paper provides empirical insights about how the different security threats at each layer can be mitigated. Originality/value The paper fulfills the need of having an extensive and elaborated survey in the field of IoT security, along with suggesting the countermeasures to mitigate the threats occurring at each level of IoT protocol stack.