Relevant bibliographies by topics / Federated devices

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Federated devices'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Federated devices"

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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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Dissertations / Theses on the topic "Federated devices"

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Kadner, Kay. "Erweiterung einer Komponentenplattform zur Unterstützung multimodaler Anwendungen mit föderierten Endgeräten." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1215608960958-10148.

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Zur Erledigung einer Aufgabe kann der Benutzer mit verschiedenen Endgeräten interagieren, welche unterschiedliche Interaktionsarten (Modalitäten) anbieten. Dabei gibt es jedoch kein Endgerät, welches alle erdenkbaren Modalitäten unterstützt. Aus diesem Grund wird eine komponentenbasierte Integrationsschicht auf Basis einer Komponentenplattform entwickelt, die dem Nutzer die gewünschte Freiheit bei der Wahl der Endgeräte und somit der Modalitäten ermöglicht. Als Ausgangsbasis dafür dient das W3C Multimodal Interaction Framework. Mit Hilfe der Integrationsschicht kann der Nutzer beispielsweise Endgeräteföderationen erzeugen, die einzeln oder gemeinsam zur Interaktion verwendet werden können. Die Integrationsschicht besitzt verschiedene Konzepte, um z.B. Geschäftslogik zur Laufzeit zu verteilen, Komponentenausfälle zu behandeln und die auf verschiedene Endgeräte verteilte Nutzerschnittstelle zu synchronisieren. Die entwickelten Konzepte wurden prototypisch implementiert, validiert und auf Performanz untersucht.
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Kadner, Kay. "Erweiterung einer Komponentenplattform zur Unterstützung multimodaler Anwendungen mit föderierten Endgeräten." Doctoral thesis, Technische Universität Dresden, 2007. https://tud.qucosa.de/id/qucosa%3A23614.

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Zur Erledigung einer Aufgabe kann der Benutzer mit verschiedenen Endgeräten interagieren, welche unterschiedliche Interaktionsarten (Modalitäten) anbieten. Dabei gibt es jedoch kein Endgerät, welches alle erdenkbaren Modalitäten unterstützt. Aus diesem Grund wird eine komponentenbasierte Integrationsschicht auf Basis einer Komponentenplattform entwickelt, die dem Nutzer die gewünschte Freiheit bei der Wahl der Endgeräte und somit der Modalitäten ermöglicht. Als Ausgangsbasis dafür dient das W3C Multimodal Interaction Framework. Mit Hilfe der Integrationsschicht kann der Nutzer beispielsweise Endgeräteföderationen erzeugen, die einzeln oder gemeinsam zur Interaktion verwendet werden können. Die Integrationsschicht besitzt verschiedene Konzepte, um z.B. Geschäftslogik zur Laufzeit zu verteilen, Komponentenausfälle zu behandeln und die auf verschiedene Endgeräte verteilte Nutzerschnittstelle zu synchronisieren. Die entwickelten Konzepte wurden prototypisch implementiert, validiert und auf Performanz untersucht.
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"Public-Private Partnerships in Saskatchewan: A Tale of Two Upgraders." Thesis, 2014. http://hdl.handle.net/10388/ETD-2014-03-1453.

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Stobbe, Mark Jacob, M.A. University of Saskatchewan, Saskatoon, Canada, February 2014. Public-Private Partnerships in Saskatchewan: A Tale of two Upgraders. Most of the literature dealing with public-private partnerships (P3s) examines the impact of private sector involvement in the provision of infrastructure or services normally provided by the public sector. This thesis uses the two case studies of the NewGrade Heavy Oil Upgrader and the Bi-Provincial Heavy Oil upgrader to examine the dynamics of P3s entered into by government in a market-driven, commercial sector for the purposes of promoting economic development. In the 1980’s, there was a political consensus in the Saskatchewan legislature that the province needed upgrading capacity to convert heavy crude oil into more marketable and valuable light synthetic crude and that the upgraders should be built through P3s. The result was the creation of the NewGrade and Bi-Provincial Upgraders. In the 1990’s, financial losses at both upgraders caused the Saskatchewan government to demand renegotiation of these partnerships. The thesis examines these partnerships in their initial negotiation, construction/operation and renegotiation in order to determine what environmental factors and internal dynamics contributed to the success or failure of the partnerships and the relations between the partners. The thesis argues that the upgraders successfully achieved their public policy objectives and gained the benefits of synergies arising from the differences between the public and private sector. However, the partnerships came under severe stress arising from a prolonged downturn in oil markets and the price of crude oil. The resulting financial losses caused the Saskatchewan government to seek a renegotiation of the terms of partnership. Despite this common cause of stress in the partnerships, the renegotiations of the agreements varied greatly. It is demonstrated that these differences arose from the financial structure of each partnership, the nature of the private sector partners and the number of partners involved in the project. The thesis provides some observations of potential value for governments and corporations considering entering partnerships for economic development projects. The differences between partners that can create synergistic benefits can also be the basis for the erosion of trust between the partners. The different financial tools used by government to participate in P3s can have significant impacts on both project viability and relationships between the partners.
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Books on the topic "Federated devices"

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Nicola, Rocco. Coordination Models and Languages: 15th International Conference, COORDINATION 2013, Held as Part of the 8th International Federated Conference on Distributed Computing Techniques, DisCoTec 2013, Florence, Italy, June 3-5, 2013. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Federated devices"

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Mut-Puigserver, Macià, M. Magdalena Payeras-Capellà, Jordi Castellà-Roca, and Llorenç Huguet-Rotger. "mCITYPASS: Privacy-Preserving Secure Access to Federated Touristic Services with Mobile Devices." In Applied Computing & Information Technology, 135–60. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64051-8_9.

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Casado, Fernando E., Dylan Lema, Roberto Iglesias, Carlos V. Regueiro, and Senén Barro. "Concept Drift Detection and Adaptation for Robotics and Mobile Devices in Federated and Continual Settings." In Advances in Intelligent Systems and Computing, 79–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62579-5_6.

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Rjoub, Gaith, Omar Abdel Wahab, Jamal Bentahar, and Ahmed Bataineh. "A Trust and Energy-Aware Double Deep Reinforcement Learning Scheduling Strategy for Federated Learning on IoT Devices." In Service-Oriented Computing, 319–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65310-1_23.

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Palma, Raul, Soumya Brahma, Christian Zinke-Wehlmann, Amit Kirschenbaum, Karel Charvát, Karel Charvat, and Tomas Reznik. "Linked Data Usages in DataBio." In Big Data in Bioeconomy, 91–111. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_8.

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AbstractOne of the main goals of DataBio was the provision of solutions for big data management enabling, among others, the harmonisation and integration of a large variety of data generated and collected through various applications, services and devices. The DataBio approach to deliver such capabilities was based on the use of Linked Data as a federated layer to provide an integrated view over (initially) disconnected and heterogeneous datasets. The large amount of data sources, ranging from mostly static to highly dynamic, led to the design and implementation of Linked Data Pipelines. The goal of these pipelines is to automate as much as possible the process to transform and publish different input datasets as Linked Data. In this chapter, we describe these pipelines and how they were applied to support different uses cases in the project, including the tools and methods used to implement them.
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Anelli, Vito Walter, Yashar Deldjoo, Tommaso Di Noia, and Antonio Ferrara. "Towards Effective Device-Aware Federated Learning." In Lecture Notes in Computer Science, 477–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35166-3_34.

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James, Frankie, and Rama Gurram. "Multimodal and Federated Interaction." In Human Computer Interaction, 102–22. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-87828-991-9.ch009.

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This chapter introduces the concepts of multimodal and federated interaction. Because multimodality means, simply, the combination of multiple modalities (or types of input and output), the authors first introduce some of the various modalities available for computer interaction. The chapter then discusses how multimodality can be used both in desktop and mobile computing environments. The goal of the chapter is to familiarize scholars and researchers with the range of topics covered under the heading “multimodality” and suggest new areas of research around the combination of modalities, as well as the combination of mobile and stationary computing devices to improve usability.
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Chakraborty, Partha, and Krishnamurthy Raghuraman. "Trends in Information Security." In Software Development Techniques for Constructive Information Systems Design, 354–76. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3679-8.ch020.

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Information systems have transitioned from being designed for sophisticated users to systems for general populace. Have information security thoughts evolved likewise? The traditional understanding of security gravitated towards physical/network/platform/security and audit logging mechanisms. This chapter looks into evolution of information security, with the current impetus towards boundary-less enterprises, federated identities, the contemporary standards, and the need for federal governments to be involved in information security, ethics, and privacy concerns. With such a gamut of influencing forces, information security needs to be inbuilt with SDLC as a natural process rather than as an afterthought. This chapter covers information security trends in relation to cloud, mobile devices, and Bring Your Own Device. Convergence of information security with risk management and business process continuity is discussed. The authors indicate a few emerging research topics in the field of information security and outline the trends for future.
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Chakraborty, Partha, and Krishnamurthy Raghuraman. "Trends in Information Security." In Standards and Standardization, 1582–604. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8111-8.ch072.

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Information systems have transitioned from being designed for sophisticated users to systems for general populace. Have information security thoughts evolved likewise? The traditional understanding of security gravitated towards physical/network/platform/security and audit logging mechanisms. This chapter looks into evolution of information security, with the current impetus towards boundary-less enterprises, federated identities, the contemporary standards, and the need for federal governments to be involved in information security, ethics, and privacy concerns. With such a gamut of influencing forces, information security needs to be inbuilt with SDLC as a natural process rather than as an afterthought. This chapter covers information security trends in relation to cloud, mobile devices, and Bring Your Own Device. Convergence of information security with risk management and business process continuity is discussed. The authors indicate a few emerging research topics in the field of information security and outline the trends for future.
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Buccella, Agustina, and Alejandra Cechich. "Current Approaches and Future Trends of Ontology-Driven Geographic Integration." In Handbook of Research on Innovations in Database Technologies and Applications, 481–90. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-242-8.ch052.

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Currently there are many domain areas in Computer Science interested in the integration of various information sources. Federated Databases, Semantic Web, and Automated Web Services are some of them. Particularly in the geographic information area, newer and better technologies and devices are being created in order to capture a large amount of information about Earth. All of this geographic information is analyzed and stored at various levels of detail in Geographic Information Systems (GISs), possibly distributed on the Web. Then a fast search for geographic information on the Web will return several links representing parts of our world. But what happens when someone needs information that is divided into more than one system? For example, information about rivers in a country can be obtained by querying two or more systems. Although distribution of information is one of the main problems, there are some others; these systems have been developed by various entities with different points of view and vocabularies, and here is when face heterogeneity problems arise. They are encountered in every communication between interoperating systems where interoperability refers to interaction between information from various sources involving the task of data integration to combine data.
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Sawyer, Steve, and Andrea Tapia. "The Sociotechnical Nature of Mobile Computing Work." In Issues and Trends in Technology and Human Interaction, 152–71. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-268-8.ch007.

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In this paper we discuss the sociotechnical nature of mobile computing as used by three policing agencies within the United States. Mobile devices, access and service was provided via a third generation wireless network to a focal application, Pennsylvania’s Justice NETwork (JNET), a secure web-based portal connecting authorized users to a set of 23 federated criminal justice and law enforcement databases via a query-based interface. In this study we conceptualize mobility and policing as a sociotechnical ensemble that builds on the social-shaping of technology perspective and the tradition of sociotechncial theorizing focusing on the co-design of work practices and technologies to support work. Drawing from the social informatics tradition, we turn a critical, empirical, and contextual lens on the practices of mobility and work. Our analysis of the data leads us to find that the social and the technical are still separate in this mobile work context. This simple view of social and technical as related, but distinct, often leads to problems with collecting and interpreting evidence of ICT-based system’s design and use. We further note this over-simplification of sociotechnical action is likely to continue unless more viable analytic approaches are developed and the assumptions of the current techno-determinist approaches challenged more explicitly.
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Conference papers on the topic "Federated devices"

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He, Xiaoxin, Xiang Su, Yang Chen, and Pan Hui. "Federated learning on wearable devices." In SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3384419.3430446.

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Xu, Zichen, Li Li, and Wenting Zou. "Exploring federated learning on battery-powered devices." In ACM TURC 2019: ACM Turing Celebration Conference - China. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3321408.3323080.

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Wei, Xiguang, Quan Li, Yang Liu, Han Yu, Tianjian Chen, and Qiang Yang. "Multi-Agent Visualization for Explaining Federated Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/960.

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As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.
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Jiang, Ziyue, Yi Ren, Ming Lei, and Zhou Zhao. "FedSpeech: Federated Text-to-Speech with Continual Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/527.

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Abstract:
Federated learning enables collaborative training of machine learning models under strict privacy restrictions and federated text-to-speech aims to synthesize natural speech of multiple users with a few audio training samples stored in their devices locally. However, federated text-to-speech faces several challenges: very few training samples from each speaker are available, training samples are all stored in local device of each user, and global model is vulnerable to various attacks. In this paper, we propose a novel federated learning architecture based on continual learning approaches to overcome the difficulties above. Specifically, 1) we use gradual pruning masks to isolate parameters for preserving speakers' tones; 2) we apply selective masks for effectively reusing knowledge from tasks; 3) a private speaker embedding is introduced to keep users' privacy. Experiments on a reduced VCTK dataset demonstrate the effectiveness of FedSpeech: it nearly matches multi-task training in terms of multi-speaker speech quality; moreover, it sufficiently retains the speakers' tones and even outperforms the multi-task training in the speaker similarity experiment.
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Pilla, Laercio Lima. "Optimal Task Assignment for Heterogeneous Federated Learning Devices." In 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2021. http://dx.doi.org/10.1109/ipdps49936.2021.00074.

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Oza, Poojan, and Vishal M. Patel. "Federated Learning-based Active Authentication on Mobile Devices." In 2021 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2021. http://dx.doi.org/10.1109/ijcb52358.2021.9484338.

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Kadner, K. "A flexible architecture for multimodal applications using federated devices." In Visual Languages and Human-Centric Computing (VL/HCC'06). IEEE, 2006. http://dx.doi.org/10.1109/vlhcc.2006.1.

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Laguel, Yassine, Krishna Pillutla, Jerome Malick, and Zaid Harchaoui. "A Superquantile Approach to Federated Learning with Heterogeneous Devices." In 2021 55th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2021. http://dx.doi.org/10.1109/ciss50987.2021.9400318.

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Wawrzonowski, Marcin, Dominik Szajerman, Marcin Daszuta, and Piotr Napieralski. "Mobile devices' GPUs in cloth dynamics simulation." In 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017. http://dx.doi.org/10.15439/2017f191.

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Gatsou, Chrysoula, Anastasios Politis, and Dimitrios Zevgolis. "Exploring users’ experience with e-reading devices." In 2015 Federated Conference on Computer Science and Information Systems. IEEE, 2015. http://dx.doi.org/10.15439/2015f197.

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