Data to 13 Oct 2026

Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

arXiv 1911.05642 ↗First posted 6 Nov 2019cs.DCcited by 75

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Abstract (arXiv)

Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. Federated learning can be a promising solution for enabling IoT-based smart applications. In this paper, we present the primary design aspects for enabling federated learning at network edge. We model the incentive-based interaction between a global server and participating devices for federated learning via a Stackelberg game to motivate the participation of the devices in the federated learning process. We present several open research challenges with their possible solutions. Finally, we provide an outlook on future research.

Key facts. Cited by 75 papers in the map (data through 13 Oct 2026). First posted on arXiv 6 Nov 2019.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 30; 2022: 11; 2023: 15; 2024: 11; 2025: 5; 2026: 3.

75 papers.

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2021Federated Learning for Smart Healthcare: A Surveycited by 183
2021Federated Learning for Open Bankingcited by 141
2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 128
2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
2021Federated Learning: Opportunities and Challengescited by 84
2021Towards Fairness-Aware Federated Learningcited by 57
2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 56
2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
2024Advances and Open Challenges in Federated Foundation Modelscited by 34
2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 29

38 more

2021Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehiclescited by 26
2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 25
2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregationcited by 22
2025Ten Challenging Problems in Federated Foundation Modelscited by 20
2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning Systemcited by 16
2021Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthinesscited by 15
2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
2021A Contract Theory based Incentive Mechanism for Federated Learningcited by 13
2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
2021Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applicationscited by 11
2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
2021Concept Drift Detection in Federated Networked Systemscited by 10
2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
2022Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspectivecited by 9
2023Backdoor Attacks in Peer-to-Peer Federated Learningcited by 9
2023Towards Practical Federated Causal Structure Learningcited by 9
2022Anomaly Detection through Unsupervised Federated Learningcited by 8
2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
2021On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestratorcited by 6
2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6
2023A Comprehensive Survey On Client Selections in Federated Learningcited by 6
2021Federated Learning: Issues in Medical Applicationcited by 4
2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
2022ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Samplingcited by 4
2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
2024Intelligent Agents for Auction-based Federated Learning: A Surveycited by 4
2024Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensorscited by 4
2021Over-the-Air Statistical Estimationcited by 3
2024Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Weldingcited by 3
2021Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Servicescited by 2
2021Management of Resource at the Network Edge for Federated Learningcited by 2
2021Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detectioncited by 2
2022An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportationcited by 2
2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
2023Byzantine-Robust Decentralized Stochastic Optimization with Stochastic Gradient Noise-Independent Learning Errorcited by 2
2021Auction Based Clustered Federated Learning in Mobile Edge Computing Systemcited by 1

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