mlsanityData to 7 Oct 2026

Over-the-Air Federated Learning and Optimization

arXiv 2310.10089 ↗First posted 16 Oct 2023cs.LG cs.IT eess.SPcited by 10

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Abstract (arXiv)

Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In this tutorial, we focus on FL via over-the-air computation (AirComp), which is proposed to reduce the communication overhead for FL over wireless networks at the cost of compromising in the learning performance due to model aggregation error arising from channel fading and noise. We first provide a comprehensive study on the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity. Through convergence and asymptotic analysis, we characterize the impact of aggregation error on the convergence bound and provide insights for system design with convergence guarantees. Then we derive convergence rates for AirFedAvg algorithms for strongly convex and non-convex objectives. For different types of local updates that can be transmitted by edge devices (i.e., local model, gradient, and model difference), we reveal that transmitting local model in AirFedAvg may cause divergence in the training procedure. In addition, we consider more practical signal processing schemes to improve the communication efficiency and further extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes. Extensive simulation results under different settings of objective functions, transmitted local information, and communication schemes verify the theoretical conclusions.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 122 of its 171 references that are in the map. First posted on arXiv 16 Oct 2023.

Builds on

122 of 171 references.

References · 122

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
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  7. 2018Federated Learning with Non-IID Datacited by 1,099
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  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
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  11. 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 716
  12. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
38 more
  1. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  2. 2021Towards Personalized Federated Learningcited by 462
  3. 2019Agnostic Federated Learningcited by 451
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  5. 2019Fair Resource Allocation in Federated Learningcited by 389
  6. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
  7. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  8. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 333
  9. 2021A Field Guide to Federated Optimizationcited by 312
  10. 2017How to Escape Saddle Points Efficientlycited by 284
  11. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  12. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  13. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  14. 2018Federated Learning via Over-the-Air Computationcited by 215
  15. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  16. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  17. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  18. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  19. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 178
  20. 2019Federated Learning over Wireless Fading Channelscited by 178
  21. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 178
  22. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  23. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  24. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 143
  25. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  26. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  27. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  28. 2020FedSplit: An algorithmic framework for fast federated optimizationcited by 108
  29. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  30. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  31. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 95
  32. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
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  34. 2018Wireless Network Intelligence at the Edgecited by 90
  35. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
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  37. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  38. 2019First Analysis of Local GD on Heterogeneous Datacited by 80

50 most cited of 122 All 122 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 3; 2026: 2.

10 papers.

  1. 2024A Survey on Integrated Sensing, Communication, and Computationcited by 12
  2. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  3. 2025Biased Federated Learning under Wireless Heterogeneitycited by 4
  4. 2025Optimal Transceiver Design in Over-the-Air Federated Distillationcited by 2
  5. 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
  6. 2024Hierarchical Learning and Computing over Space-Ground Integrated Networkscited by 1
  7. 2025Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-offcited by 1
  8. 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0
  9. 2026SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillationcited by 0
  10. 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learningcited by 0