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Convergence Analysis and System Design for Federated Learning over Wireless Networks

arXiv 2105.00872 ↗First posted 30 Apr 2021cs.LG cs.DC cs.MAcited by 25

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Abstract (arXiv)

Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. However, as the training data in FL is not collected and stored centrally, FL training requires frequent model exchange, which is largely affected by the wireless communication network. Therein, limited bandwidth and random package loss restrict interactions in training. Meanwhile, the insufficient message synchronization among distributed clients could also affect FL convergence. In this paper, we analyze the convergence rate of FL training considering the joint impact of communication network and training settings. Further by considering the training costs in terms of time and power, the optimal scheduling problems for communication networks are formulated. The developed theoretical results can be used to assist the system parameter selections and explain the principle of how the wireless communication system could influence the distributed training process and network scheduling.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 12 of its 15 references that are in the map. First posted on arXiv 30 Apr 2021.

Builds on

12 of 15 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  4. 2016Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Conditioncited by 526
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  6. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  8. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  9. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  10. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  11. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  12. 2019Accelerating DNN Training in Wireless Federated Edge Learning Systemscited by 34

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 8; 2024: 9; 2025: 0; 2026: 1.

25 papers.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  3. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
  4. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  5. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
  6. 2022Communication-Efficient Federated Distillation with Active Data Samplingcited by 10
  7. 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
  8. 2023Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Predictioncited by 9
  9. 2024Mobility-Aware Federated Self-supervised Learning in Vehicular Networkcited by 9
  10. 2024DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVcited by 7
  11. 2021How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learningcited by 6
  12. 2024Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocationcited by 5
13 more
  1. 2022ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Samplingcited by 4
  2. 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4
  3. 2023Dynamic Scheduling for Federated Edge Learning with Streaming Datacited by 3
  4. 2024Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocationcited by 3
  5. 2023FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularizationcited by 2
  6. 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
  7. 2024Optimal Batch Allocation for Wireless Federated Learningcited by 1
  8. 2026Decentralized Federated Learning With Energy Harvesting Devicescited by 1
  9. 2022Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updatescited by 0
  10. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0
  11. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0
  12. 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradientcited by 0
  13. 2023Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Frameworkcited by 0