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Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation

arXiv 1910.13067 ↗First posted 29 Oct 2019cs.LG cs.DC cs.NIcited by 94

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Abstract (arXiv)

There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data. Despite its advantages in data privacy-preserving, Federated Learning (FL) still has challenges in heterogeneity across UEs' data and physical resources. We first propose a FL algorithm which can handle the heterogeneous UEs' data challenge without further assumptions except strongly convex and smooth loss functions. We provide the convergence rate characterizing the trade-off between local computation rounds of UE to update its local model and global communication rounds to update the FL global model. We then employ the proposed FL algorithm in wireless networks as a resource allocation optimization problem that captures the trade-off between the FL convergence wall clock time and energy consumption of UEs with heterogeneous computing and power resources. Even though the wireless resource allocation problem of FL is non-convex, we exploit this problem's structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights to problem design. Finally, we illustrate the theoretical analysis for the new algorithm with Tensorflow experiments and extensive numerical results for the wireless resource allocation sub-problems. The experiment results not only verify the theoretical convergence but also show that our proposed algorithm outperforms the vanilla FedAvg algorithm in terms of convergence rate and testing accuracy.

Key facts. Cited by 94 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Oct 2019.

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Citing papers per year: 2021: 21; 2022: 22; 2023: 23; 2024: 13; 2025: 10; 2026: 5.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  2. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  3. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  4. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  5. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39
  6. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  7. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  8. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  9. 2023Green Edge AI: A Contemporary Surveycited by 25
  10. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
  11. 2023Bayesian Federated Learning: A Surveycited by 22
  12. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
38 more
  1. 2021Decentralized Inference with Graph Neural Networks in Wireless Communication Systemscited by 18
  2. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  3. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  4. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  5. 2021Optimal Rate Adaption in Federated Learning with Compressed Communicationscited by 15
  6. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  7. 2024Federated Distillation: A Surveycited by 15
  8. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  9. 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
  10. 2021Federated Learning over Wireless IoT Networks with Optimized Communication and Resourcescited by 13
  11. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
  12. 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
  13. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  14. 2021Federated Few-Shot Learning with Adversarial Learningcited by 11
  15. 2021Hierarchical Over-the-Air Federated Edge Learningcited by 11
  16. 2021Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updatescited by 10
  17. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  18. 2022Time-triggered Federated Learning over Wireless Networkscited by 10
  19. 2022Over-the-Air Federated Edge Learning with Hierarchical Clusteringcited by 10
  20. 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devicescited by 10
  21. 2022Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Realitycited by 9
  22. 2021FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systemscited by 6
  23. 2022SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networkscited by 6
  24. 2022Federated Distillation based Indoor Localization for IoT Networkscited by 6
  25. 2023EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learningcited by 6
  26. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
  27. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
  28. 2025AugFL: Augmenting Federated Learning with Pretrained Modelscited by 6
  29. 2022Towards Communication-Learning Trade-off for Federated Learning at the Network Edgecited by 5
  30. 2022A Fast Blockchain-based Federated Learning Framework with Compressed Communicationscited by 5
  31. 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
  32. 2022Social-Aware Clustered Federated Learning with Customized Privacy Preservationcited by 5
  33. 2023Contrastive encoder pre-training-based clustered federated learning for heterogeneous datacited by 5
  34. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  35. 2022Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Codingcited by 4
  36. 2023A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniquescited by 4
  37. 2023Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computingcited by 4
  38. 2023Faster Federated Learning with Decaying Number of Local SGD Stepscited by 4

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