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Articoli di riviste sul tema "Federated network"

Autore: Grafiati

Pubblicato: 25 gennaio 2025

Ultima modifica: 31 luglio 2025

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1

Шубин, Б., Т. Максимюк, О. Яремко, Л. Фабрі та Д. Мрозек. "МОДЕЛЬ ІНТЕГРАЦІЇ ФЕДЕРАТИВНОГО НАВЧАННЯ В МЕРЕЖІ МОБІЛЬНОГО ЗВ’ЯЗКУ 5-ГО ПОКОЛІННЯ". Information and communication technologies, electronic engineering 2, № 1 (2022): 26–35. http://dx.doi.org/10.23939/ictee2022.01.026.

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This paper investigates the main advantages of using Federated Learning (FL) for sharing experiences between intelligent devices in the environment of 5th generation mobile communication networks. This approach makes it possible to build effective machine learning algorithms using confidential data, the loss of which may be undesirable or even dangerous for users. Therefore, for the tasks where the confidentiality of the data is required for processing and analysis, we suggest using Federated Learning (FL) approaches. In this case, all users' personal information will be processed locally on t

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Zhang, Kainan, Zhipeng Cai, and Daehee Seo. "Privacy-Preserving Federated Graph Neural Network Learning on Non-IID Graph Data." Wireless Communications and Mobile Computing 2023 (February 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/8545101.

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Since the concept of federated learning (FL) was proposed by Google in 2017, many applications have been combined with FL technology due to its outstanding performance in data integration, computing performance, privacy protection, etc. However, most traditional federated learning-based applications focus on image processing and natural language processing with few achievements in graph neural networks due to the graph’s nonindependent identically distributed (IID) nature. Representation learning on graph-structured data generates graph embedding, which helps machines understand graphs effecti

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Hang, Yifei. "Federated learning-based neural network for hotel cancellation prediction." Applied and Computational Engineering 45, no. 1 (2024): 190–95. http://dx.doi.org/10.54254/2755-2721/45/20241092.

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Hotel reservations have become a prevalent choice for customers. However, cancellations of these reservations present a significant challenge for hotels, potentially resulting in financial losses and a decline in customer satisfaction. To address the issue of improper management of cancellations and minimize losses, machine learning can be employed to analyze and predict cancellations based on customer information. In cooperative scenarios where hotels collaborate to train a unified model, traditional algorithms that aggregate all data raise concerns about the protection of sensitive customer

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Yu, Yun William, and Griffin M. Weber. "Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation." Journal of Medical Internet Research 22, no. 11 (2020): e18735. http://dx.doi.org/10.2196/18735.

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Background Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link pa

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Kostenko, Valery Alekseevich, and Alisa Evgenievna Selezneva. "Types of Attacks on Federated Neural Networks and Methods of Protection." Proceedings of the Institute for System Programming of the RAS 36, no. 1 (2024): 35–44. http://dx.doi.org/10.15514/ispras-2024-36(1)-3.

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Federated learning is a technology for privacy-preserving learning in distributed storage systems. This training allows you to create a general forecasting model, storing all the data in your storage systems. Several devices take part in training the general model, and each device has its own unique data on which the neural network is trained. The interaction of devices occurs only to adjust the weights of the general model. After which, the updated model is transmitted to all devices. Training on multiple devices creates many attack opportunities against this type of network. After training o

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Ma, Xiaoyu, and Lize Gu. "Research and Application of Generative-Adversarial-Network Attacks Defense Method Based on Federated Learning." Electronics 12, no. 4 (2023): 975. http://dx.doi.org/10.3390/electronics12040975.

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In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. This paper proposes Defense-GAN, a defense method against Generative Adversarial Network attacks under Federated Learning. Under this method, the attacker cannot learn the real image data distribution. Each Federated Learning participant uses SHAP to explain the model and masks the pixel features that have a greater im

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Tian, Mengmeng. "An Contract Theory based Federated Learning Aggregation Algorithm in IoT Network." Journal of Physics: Conference Series 2258, no. 1 (2022): 012008. http://dx.doi.org/10.1088/1742-6596/2258/1/012008.

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Abstract Taking IoT devices as the edge nodes is one of the powerful way to offloading the federated task since IoT devices are closer to the data generation end. The aggregation efficiency of federated learning in the IoT environment is inefficiency since the server of federated learning can not know the data quality of heterogeneous IoT device. How to encourage IoT edge clients to participate in federated learning and maximize the aggregation effect of the global model is an important problem. This paper proposes a federated learning aggregation model based on contract theory incentive mecha

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Al-Tameemi, M., M. B. Hassan, and S. A. Abass. "Federated Learning (FL) – Overview." LETI Transactions on Electrical Engineering & Computer Science 17, no. 5 (2024): 74–82. http://dx.doi.org/10.32603/2071-8985-2024-17-5-74-82.

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Explores the fundamental aspects of federated learning (FL) in the context of intrusion detection systems (IDS) within Internet of Things (IoT) networks. Federated learning presents an innovative approach to training machine learning models on distributed devices, thereby minimizing the need to transmit sensitive data to central servers. We classify FL into horizontal, vertical, and federated transfer learning and examine their application in IDS systems. Additionally, we analyze the network structure of FL, encompassing centralized and decentralized FL. Based on the conducted review, it can b

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Rizzato, Matteo, Youssef Laarouchi, and Christophe Geissler. "Using Federated Learning for Collaborative Intrusion Detection Systems." Journal of Systemics, Cybernetics and Informatics 21, no. 3 (2023): 29–36. http://dx.doi.org/10.54808/jsci.21.03.29.

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Neural networks have become cutting edge machine learning models for detecting network attacks. Traditional implementations provide fast and accurate predictions, but require centralised storage of labelled historical data for training. This solution is not always suitable for real-world applications, where regulatory constraints and privacy concerns hamper the collection of sensitive data into a single server. Federated Learning has recently been proposed as a framework for training a centralised model without the need to share data between different providers. We use the CICIDS2017 dataset p

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Wang, Shuangzhong, and Ying Zhang. "Multi-Level Federated Network Based on Interpretable Indicators for Ship Rolling Bearing Fault Diagnosis." Journal of Marine Science and Engineering 10, no. 6 (2022): 743. http://dx.doi.org/10.3390/jmse10060743.

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The federated learning network requires all the connection weights to be shared among the server and clients during training which increases the risk of data leakage. Meanwhile, the traditional federated learning method has a poor diagnostic effect for non-independently identically distributed data. In order to address these issues, a multi-level federated network based on interpretable indicators was proposed in this manuscript. Firstly, an interpretable adaptive sparse deep network is constructed based on the interpretability principle. Secondly, the relevance map of the network is construct

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Liu, Jingxin, Jieren Cheng, Renda Han, Wenxuan Tu, Jiaxin Wang, and Xin Peng. "Federated Graph-Level Clustering Network." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 18870–78. https://doi.org/10.1609/aaai.v39i18.34077.

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Federated graph learning (FGL), which excels in analyzing non-IID graphs as well as protecting data privacy, has recently emerged as a hot topic. Existing FGL methods usually train the client model using labeled data and then collaboratively learn a global model without sharing their local graph data. However, in real-world scenarios, the lack of data annotations impedes the negotiation of multi-source information at the server, leading to sub-optimal feedback to the clients. To address this issue, we propose a novel unsupervised learning framework called Federated Graph-level Clustering Netwo

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Ramesh, K. "Q-AFL: A Quantum-Inspired Adaptive Federated Learning Framework for Wireless Network Optimization." International Journal of Advanced Research in Science and Technology 14, no. 5 (2025): 1570–75. https://doi.org/10.62226/ijarst2024132548.

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Wireless networks, especially with the evolution toward 6G and beyond, face unprecedented demands for efficiency, security, and adaptability in handling massive data exchanges across diverse, distributed, and resource-constrained devices. Conventional centralized learning paradigms present significant limitations due to high communication overhead, privacy concerns, and suboptimal adaptability to dynamic network environments. To overcome these challenges, this research proposes a novel Quantum-inspired Adaptive Federated Learning (Q-AFL) framework designed specifically to optimize wireless net

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Meeker, Daniella, Xiaoqian Jiang, Michael E. Matheny, et al. "A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research." Journal of the American Medical Informatics Association 22, no. 6 (2015): 1187–95. http://dx.doi.org/10.1093/jamia/ocv017.

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Abstract Background Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. Objective The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow

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Park, Sunghwan, Yeryoung Suh, and Jaewoo Lee. "FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs." Sensors 21, no. 2 (2021): 600. http://dx.doi.org/10.3390/s21020600.

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Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights,

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Luo, Yihang, Bei Gong, Haotian Zhu, and Chong Guo. "A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security." Applied Sciences 13, no. 19 (2023): 10586. http://dx.doi.org/10.3390/app131910586.

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The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a direct

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Calo, James, and Benny Lo. "Federated Blockchain Learning at the Edge." Information 14, no. 6 (2023): 318. http://dx.doi.org/10.3390/info14060318.

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Machine learning, particularly using neural networks, is now widely adopted in practice even with the IoT paradigm; however, training neural networks at the edge, on IoT devices, remains elusive, mainly due to computational requirements. Furthermore, effective training requires large quantities of data and privacy concerns restrict accessible data. Therefore, in this paper, we propose a method leveraging a blockchain and federated learning to train neural networks at the edge effectively bypassing these issues and providing additional benefits such as distributing training across multiple devi

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Rezazadeh, F., L. Zanzi, F. Devoti, H. Chergui, X. Costa-Perez, and C. Verikoukis. "On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration." IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 72, no. 3 (2023): 3473–87. https://doi.org/10.1109/TVT.2022.3218158.

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Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (D

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Naeem, Muhammad Ali, Yahui Meng, and Sushank Chaudhary. "The Impact of Federated Learning on Improving the IoT-Based Network in a Sustainable Smart Cities." Electronics 13, no. 18 (2024): 3653. http://dx.doi.org/10.3390/electronics13183653.

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The caching mechanism of federated learning in smart cities is vital for improving data handling and communication in IoT environments. Because it facilitates learning among separately connected devices, federated learning makes it possible to quickly update caching strategies in response to data usage without invading users’ privacy. Federated learning caching promotes improved dynamism, effectiveness, and data reachability for smart city services to function properly. In this paper, a new caching strategy for Named Data Networking (NDN) based on federated learning in smart cities’ IoT contex

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Liu, Zhetong, Qiugang Zhan, Xiurui Xie, Bingchao Wang, and Guisong Liu. "Federal SNN Distillation: A Low-Communication-Cost Federated Learning Framework for Spiking Neural Networks." Journal of Physics: Conference Series 2216, no. 1 (2022): 012078. http://dx.doi.org/10.1088/1742-6596/2216/1/012078.

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Abstract In recent years, research on the federated spiking neural network (SNN) framework has attracted increasing attention in the area of on-chip learning for embedded devices, because of its advantages of low power consumption and privacy security. Most of the existing federated SNN frameworks are based on the classical federated learning framework -- Federated Average (FedAvg) framework, where internal communication is achieved by exchanging network parameters or gradients. However, although these frameworks take a series of methods to reduce the communication cost, the communication of f

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Tang, Jiayi, Wenxin Li, Qinchen Zhao, and Hongmei Chi. "Federated-Learning-Based Strategy for Enhancing Orbit Prediction of Satellites." Mathematics 13, no. 8 (2025): 1312. https://doi.org/10.3390/math13081312.

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As the primary public source of satellite trajectory data, the Two-Line Element (TLE) dataset offers fundamental orbital parameters for space missions. However, for satellites with poor data quality, traditional neural network models often underperform, hindering accurate orbit predictions and meeting demands in satellite operation and space mission planning. To address this, a federated-learning-based trajectory prediction enhancement strategy is proposed. Satellites with low training efficiency and similar orbits are grouped for collaborative learning. Each satellite uses a Convolutional Neu

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Sathishkumar, Mani, Chandrasekaran Kishoreraja Parasuram, Joseph Christeena, Manoharan Reji, and Theerthagiri Prasannavenkatesan. "Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 492–99. https://doi.org/10.11591/ijai.v14.i1.pp492-499.

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The applications of wireless sensor networks are vast and popular in today’s technology world. These networks consist of small, independent sensors that are capable of measuring various physical quantities. Deployment of wireless sensor networks increased due to immense applications which are susceptible to different types of attacks in an unprotected and open region. Intrusion detection systems (IDS) play a vital part in any secured environment for any network. IDS using federated learning have the potential to achieve better classification accuracy. Usually, all the data is stored in c

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Estiri, Hossein, Jeffrey G. Klann, Sarah R. Weiler, et al. "A federated EHR network data completeness tracking system." Journal of the American Medical Informatics Association 26, no. 7 (2019): 637–45. http://dx.doi.org/10.1093/jamia/ocz014.

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Abstract Objective The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. Materials and Methods The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source t

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Zou, Qianying, Yushi Li, Xinyue Jiang, Yuepeng Zan, and Fengyu Liu. "Network Intrusion Detection Based on Convolutional Recurrent Neural Network, Random Forest, and Federated Learning." Journal of Computing and Information Technology 32, no. 2 (2024): 97–125. http://dx.doi.org/10.20532/cit.2024.1005838.

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This paper presents a novel network intrusion detection framework that combines convolutional recurrent neural networks (CRNN) and random forest (RF) models within a federated learning setting. The proposed approach aims to address the challenges of data privacy, computational efficiency, and model generalization in traditional network intrusion detection methods. By leveraging the spatial feature extraction capabilities of CRNN and the feature selection and noise reduction properties of RF, the framework enhances the accuracy and robustness of attack detection. The integration of federated le

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Massingham, Peter. "Australia's Federated Network Universities: What happened?" Journal of Higher Education Policy and Management 23, no. 1 (2001): 19–32. http://dx.doi.org/10.1080/13600800020047216.

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Zheng, Han. "Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models." ITM Web of Conferences 70 (2025): 01022. https://doi.org/10.1051/itmconf/20257001022.

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Because of the privacy concerns about the transaction data, it is essential not to leak it when training prediction models for credit card fraud analysis. Challenges for credit card fraud monitoring include highly imbalanced datasets and the need for advanced models to detect fraud patterns. This paper introduced federated learning and discussed a few federated learning algorithms applied to the problem—these methods include Federated Graph Attention Network with Dilated Convolution Neural Network (FedGAT-DCNN), FedAvg with Convolutional Neural Network (CNN), and Federated Averaging with Dista

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Chen, Naiyue, Yi Jin, Yinglong Li, and Luxin Cai. "Trust-based federated learning for network anomaly detection." Web Intelligence 19, no. 4 (2022): 317–27. http://dx.doi.org/10.3233/web-210475.

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With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to th

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Mahamad, Habiba. "GUARDIANS OF THE DATA GALAXY: A FEDERATED AI AND CLOUD SYNERGY FOR ZERO-TRUST CYBERSECURITY MODELS." International Journal of Education Humanities and Social Science 07, no. 06 (2024): 796–810. https://doi.org/10.54922/ijehss.2024.0746.

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As cyber-attacks evolve, traditional centralized security paradigms struggle to offer data privacy, scalability, and real-time threat detection across distributed systems. This paper introduces a novel solution that combines Federated Artificial Intelligence with Zero-Trust security principles to create a secure, decentralized cybersecurity system. The proposed methodology integrates a Network Intrusion dataset from Kaggle with real-time and synthetic data collected from enterprise networks, IoT devices, and cloud infrastructures. With the simulation of an actual attack surface, this setting a

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Karras, Aristeidis, Anastasios Giannaros, Leonidas Theodorakopoulos, et al. "FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE." Electronics 12, no. 22 (2023): 4633. http://dx.doi.org/10.3390/electronics12224633.

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In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model cons

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Liu, Fengchun, Meng Li, Xiaoxiao Liu, Tao Xue, Jing Ren, and Chunying Zhang. "A Review of Federated Meta-Learning and Its Application in Cyberspace Security." Electronics 12, no. 15 (2023): 3295. http://dx.doi.org/10.3390/electronics12153295.

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In recent years, significant progress has been made in the application of federated learning (FL) in various aspects of cyberspace security, such as intrusion detection, privacy protection, and anomaly detection. However, the robustness of federated learning in the face of malicious attacks (such us adversarial attacks, backdoor attacks, and poisoning attacks) is weak, and the unfair allocation of resources leads to slow convergence and inefficient communication efficiency regarding FL models. Additionally, the scarcity of malicious samples during FL model training and the heterogeneity of dat

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Ennaji, El Mahfoud, Salah El Hajla, Yassine Maleh, and Soufyane Mounir. "Adversarially robust federated deep learning models for intrusion detection in IoT." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (2025): 937. http://dx.doi.org/10.11591/ijeecs.v37.i2.pp937-947.

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<span>Ensuring the robustness, security, and privacy of machine learning is a pivotal objective, crucial for unlocking the complete potential of the internet of things (IoT). Deep neural networks have proven to be vulnerable to adversarial perturbations imperceptible to humans. These perturbations can give rise to adversarial attacks, leading to erroneous predictions by deep neural networks, particularly in intrusion detection within the IoT environment. This paper introduces a federated adversarial learning framework designed to protect both data privacy and deep neural network models.

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El, Mahfoud Ennaji Salah El Hajla Yassine Maleh Soufyane Mounir. "Adversarially robust federated deep learning models for intrusion detection in IoT." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (2025): 937–47. https://doi.org/10.11591/ijeecs.v37.i2.pp937-947.

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Ensuring the robustness, security, and privacy of machine learning is a pivotal objective, crucial for unlocking the complete potential of the internet of things (IoT). Deep neural networks have proven to be vulnerable to adversarial perturbations imperceptible to humans. These perturbations can give rise to adversarial attacks, leading to erroneous predictions by deep neural networks, particularly in intrusion detection within the IoT environment. This paper introduces a federated adversarial learning framework designed to protect both data privacy and deep neural network models. This framewo

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Khan, Urooj Yousuf, Musharaf Ali Talpur, Umme Laila, and Samar Raza Talpur. "Analysis of Energy Consumption in a Federated Learning-Based Zero-Touch Network." Sir Syed University Research Journal of Engineering & Technology 15, no. 1 (2025): 50–57. https://doi.org/10.33317/ssurj.676.

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The current world revolves around data. Internet predicts that there are currently 2.8 million devices connected to the Internet. It spans the largest web with almost six connected devices per person. Cloud infrastructure is reaching its maximum capacity and hence needs upgrading. Fog Computing is a viable addition. This infrastructure upgrade also includes a suitable routing algorithm and its complement switching topologies. Adding self-learning capabilities to such a network implies the notion of Zero-Touch Networks. A pivotal point in Zero-Touch Networks is the selection of an optimal machi

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Dongkyun Kim, Gicheol Wang, GiSung Yoo, SeungHae Kim, and OkHwan Byeon. "Media-Specific Network Service Environment on Federated Autonomous Distributed Networks." International Journal of Advancements in Computing Technology 5, no. 1 (2013): 659–67. http://dx.doi.org/10.4156/ijact.vol5.issue1.73.

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Dahir, Mohamed Haji, Hadi Alizadeh, and Didem Gözüpek. "Energy efficient virtual network embedding for federated software-defined networks." International Journal of Communication Systems 32, no. 6 (2019): e3912. http://dx.doi.org/10.1002/dac.3912.

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Mani, Sathishkumar, Parasuram Chandrasekaran Kishoreraja, Christeena Joseph, Reji Manoharan, and Prasannavenkatesan Theerthagiri. "Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 492. http://dx.doi.org/10.11591/ijai.v14.i1.pp492-499.

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<p class="Abstract">The applications of wireless sensor networks are vast and popular in today’s technology world. These networks consist of small, independent sensors that are capable of measuring various physical quantities. Deployment of wireless sensor networks increased due to immense applications which are susceptible to different types of attacks in an unprotected and open region. Intrusion detection systems (IDS) play a vital part in any secured environment for any network. IDS using federated learning have the potential to achieve better classification accuracy. Usually, all the

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Wang, Yunhui, Weichu Zheng, Zifei Liu, et al. "A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation." Electronics 12, no. 19 (2023): 4049. http://dx.doi.org/10.3390/electronics12194049.

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The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts of data being generated. Also, artificial intelligence technology, like machine learning and deep learning, is widely used to mine the value of the data. Specifically, detecting attacks on the cloud–fog–edge computing system using mobile devices is essential. External attacks on network press organizations led to anomaly flow in network traffic. The network intrusion detection system (NIDS) has been an effective method for detecting anomaly flow. However, the NIDS is hard to deploy in distributed ne

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Hemalatha B M, Sharath M N, and Lohith D K. "Blockchain enabled secure federated learning framework." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 1640–48. https://doi.org/10.30574/wjaets.2025.15.3.0335.

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Abstract (sommario):

Federate machine learning (FML) is a novel concept that trains the model to leverage data from many users rather than store the data. Federated learning (FL) allows participants to be involved without disclosing sensitive data to train the model. The server will initialize the global model with all connected participants. After the initialization, the initial global model gets trained locally with the participant’s local data set. The level of security directly affects or impacts the overall performance of the FML. Also, many security frameworks in FML are designed to handle specific types of

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R. Sushmitha. "Adaptive Blockchain-Integrated Nonlinear Federated Learning Framework for Real-Time Intrusion Detection in IoT Fog Networks ABFL-RTID." Communications on Applied Nonlinear Analysis 32, no. 1s (2024): 105–21. http://dx.doi.org/10.52783/cana.v32.2113.

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Abstract (sommario):

This research presents the Adaptive Blockchain-Integrated Nonlinear Federated Learning (ABFL-RTID) model, designed for real-time intrusion detection in IoT fog networks. The framework leverages nonlinear learning techniques, such as deep neural networks, to enhance detection capabilities in complex and dynamic network environments. Integrating blockchain ensures decentralized security, while federated learning preserves data privacy by enabling local model training on edge devices. The nonlinear models improve adaptability, accurately identifying sophisticated intrusion patterns while securely

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Youm, Sungkwan, and Taeyoon Kim. "Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling." Applied Sciences 15, no. 9 (2025): 5067. https://doi.org/10.3390/app15095067.

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Abstract (sommario):

Federated Learning (FL) presents a promising approach for collaborative intrusion detection while preserving data privacy. However, current FL frameworks face challenges with non-independent and identically distributed (non-IID) data and class imbalances in network security contexts. This paper introduces Dynamic Sampling-FedIDS (DS-FedIDS), a novel framework that enhances federated intrusion detection through adaptive sampling and personalization. DS-FedIDS extends the Federated Learning with Personalization Layers (FedPer) architecture by incorporating dynamic up/down sampling to address cla

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Fan, Kefeng, Cun Xu, Xuguang Cao, Kaijie Jiao, and Wei Mo. "Tri-branch feature pyramid network based on federated particle swarm optimization for polyp segmentation." Mathematical Biosciences and Engineering 21, no. 1 (2024): 1610–24. http://dx.doi.org/10.3934/mbe.2024070.

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Abstract (sommario):

<abstract><p>Deep learning technology has shown considerable potential in various domains. However, due to privacy issues associated with medical data, legal and ethical constraints often result in smaller datasets. The limitations of smaller datasets hinder the applicability of deep learning technology in the field of medical image processing. To address this challenge, we proposed the Federated Particle Swarm Optimization algorithm, which is designed to increase the efficiency of decentralized data utilization in federated learning and to protect privacy in model training. To sta

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Wang, Xiujuan, Kangmiao Chen, Keke Wang, Zhengxiang Wang, Kangfeng Zheng, and Jiayue Zhang. "FedKG: A Knowledge Distillation-Based Federated Graph Method for Social Bot Detection." Sensors 24, no. 11 (2024): 3481. http://dx.doi.org/10.3390/s24113481.

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Abstract (sommario):

Malicious social bots pose a serious threat to social network security by spreading false information and guiding bad opinions in social networks. The singularity and scarcity of single organization data and the high cost of labeling social bots have given rise to the construction of federated models that combine federated learning with social bot detection. In this paper, we first combine the federated learning framework with the Relational Graph Convolutional Neural Network (RGCN) model to achieve federated social bot detection. A class-level cross entropy loss function is applied in the loc

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Wang, Weidong, Siqi Li, Jihao Zhang, Dan Shan, Guangwei Zhang, and Xiang Gao. "A Node Selection Strategy in Space-Air-Ground Information Networks: A Double Deep Q-Network Based on the Federated Learning Training Method." Remote Sensing 16, no. 4 (2024): 651. http://dx.doi.org/10.3390/rs16040651.

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Abstract (sommario):

The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these ch

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Zhao, Zhuoyue, Feiyu Wu, Chao Dong, and Yuben Qu. "Embedded Implementation and Evaluation of Deep Neural Network of Federated Learning." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 687–94. http://dx.doi.org/10.54097/hset.v39i.6628.

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Abstract (sommario):

Compared with traditional distributed machine learning, federated learning (or joint learning) enables multiple computing nodes to cooperate and train a shared machine learning model without transmitting original data. At present, the research work of federated learning mainly focuses on the theoretical method, and the system implementation is less, and only for the text data or simple image such as medical institution information sharing, handwriting font recognition and other simple neural network applications. Aiming at more complex deep neural networks, this project implements a multi-node

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Xiaoyu Lan, Jalil Taghia, Farnaz Moradi, et al. "Federated learning for performance prediction in multi-operator environments." ITU Journal on Future and Evolving Technologies 4, no. 1 (2023): 166–77. http://dx.doi.org/10.52953/pfyz9165.

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Abstract (sommario):

Telecom vendors and operators deliver services with strict requirements on performance, over complex and sometimes partly shared network infrastructures. A key enabler for network and service management in such environments is knowledge sharing, and the use of data-driven models for performance prediction, forecasting, and troubleshooting. In this paper, we outline a multi-operator service metrics prediction framework using federated learning that allows privacy-preserved knowledge-sharing across operators for improved model performance, and also reduced requirements on data transfer within an

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Fathalla, Efat, and Mohamed Azab. "Decentralized Trace-Resistant Self-Sovereign Service Provisioning for Next-Generation Federated Wireless Networks." Information 16, no. 3 (2025): 159. https://doi.org/10.3390/info16030159.

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Abstract (sommario):

With the advent of NextG wireless networks, the reliance on centralized identity and service management systems poses significant challenges, including limited interoperability, increased privacy vulnerabilities, and the risk of unauthorized tracking or monitoring of user activity. To address these issues, there is a critical need for a decentralized framework that empowers users with self-sovereignty over their subscription information while maintaining trust and privacy among network entities. This article presents a novel framework to enable Self-Sovereign Federated NextG (SSFXG) wireless c

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Jiang, Jingyan, Liang Hu, Chenghao Hu, Jiate Liu, and Zhi Wang. "BACombo—Bandwidth-Aware Decentralized Federated Learning." Electronics 9, no. 3 (2020): 440. http://dx.doi.org/10.3390/electronics9030440.

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Abstract (sommario):

The emerging concern about data privacy and security has motivated the proposal of federated learning. Federated learning allows computing nodes to only synchronize the locally- trained models instead of their original data in distributed training. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized typologies and large nodes-to-server bandwidths. However, in real-world federated learning scenarios, the network capacities between nodes are highly uniformly distributed and smaller than that in data centers. As a result, how to e

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Gao, Fuwei, Chuanting Zhang, Jingping Qiao, Kaiqiang Li, and Yi Cao. "Communication-Efficient Wireless Traffic Prediction with Federated Learning." Mathematics 12, no. 16 (2024): 2539. http://dx.doi.org/10.3390/math12162539.

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Abstract (sommario):

Wireless traffic prediction is essential to developing intelligent communication networks that facilitate efficient resource allocation. Along this line, decentralized wireless traffic prediction under the paradigm of federated learning is becoming increasingly significant. Compared to traditional centralized learning, federated learning satisfies network operators’ requirements for sensitive data protection and reduces the consumption of network resources. In this paper, we propose a novel communication-efficient federated learning framework, named FedCE, by developing a gradient compression

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Duan, Shaoming, Chuanyi Liu, Peiyi Han, et al. "Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in Federated Learning." Security and Communication Networks 2023 (February 23, 2023): 1–14. http://dx.doi.org/10.1155/2023/5968168.

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Abstract (sommario):

Federated learning is a distributed machine learning framework that has been widely applied in scenarios that require data privacy. To obtain a neural network model that performs well, when the model falls into a bug, existing solutions retrain it on a larger training dataset or the carefully selected samples from model diagnosis. To overcome this challenge, this paper presents Fed-DNN-Debugger, which can automatically and efficiently fix DNN models in federated learning. Fed-DNN-Debugger fixes the federated model by fixing each client model. Fed-DNN-Debugger consists of two modules for debugg

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Wang, Derui, Sheng Wen, Alireza Jolfaei, Mohammad Sayad Haghighi, Surya Nepal, and Yang Xiang. "On the Neural Backdoor of Federated Generative Models in Edge Computing." ACM Transactions on Internet Technology 22, no. 2 (2022): 1–21. http://dx.doi.org/10.1145/3425662.

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Abstract (sommario):

Edge computing, as a relatively recent evolution of cloud computing architecture, is the newest way for enterprises to distribute computational power and lower repetitive referrals to central authorities. In the edge computing environment, Generative Models (GMs) have been found to be valuable and useful in machine learning tasks such as data augmentation and data pre-processing. Federated learning and distributed learning refer to training machine learning models in the edge computing network. However, federated learning and distributed learning also bring additional risks to GMs since all pe

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Juan, Pin-Hung, and Ja-Ling Wu. "Enhancing Communication Efficiency and Training Time Uniformity in Federated Learning through Multi-Branch Networks and the Oort Algorithm." Algorithms 17, no. 2 (2024): 52. http://dx.doi.org/10.3390/a17020052.

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Abstract (sommario):

In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non-iid data, a challenge not adequately tackled by the commonly used MFedAvg method. Additionally, one of the key innovations of this research is the introduction of uniformity, a metric that quantifies the disparity in training time amongst participants in a federated learning setup. This novel concept not only aids in identifying stragglers but

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