mlsanityData to 7 Oct 2026

Fast Convergence Algorithm for Analog Federated Learning

arXiv 2011.06658 ↗First posted 30 Oct 2020cs.IT cs.LG eess.SPcited by 16

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Abstract (arXiv)

In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp). To realize efficient analog federated learning over wireless channels, we propose an AirComp-based FedSplit algorithm, where a threshold-based device selection scheme is adopted to achieve reliable local model uploading. In particular, we analyze the performance of the proposed algorithm and prove that the proposed algorithm linearly converges to the optimal solutions under the assumption that the objective function is strongly convex and smooth. We also characterize the robustness of proposed algorithm to the ill-conditioned problems, thereby achieving fast convergence rates and reducing communication rounds. A finite error bound is further provided to reveal the relationship between the convergence behavior and the channel fading and noise. Our algorithm is theoretically and experimentally verified to be much more robust to the ill-conditioned problems with faster convergence compared with other benchmark FL algorithms.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 8; 2024: 2.

16 papers.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2022Accelerated Federated Learning with Decoupled Adaptive Optimizationcited by 23
  3. 2023On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharingcited by 16
  4. 2022Over-the-Air Federated Learning via Second-Order Optimizationcited by 11
  5. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  6. 2021Federated Learning over Noisy Channels: Convergence Analysis and Design Examplescited by 8
  7. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  8. 2021BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackerscited by 6
  9. 2023Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noisecited by 4
  10. 2023AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memorycited by 4
  11. 2024Adaptive Federated Learning Over the Aircited by 4
  12. 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detectioncited by 4
4 more
  1. 2023How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channelscited by 2
  2. 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
  3. 2023Over-the-Air Federated Averaging with Limited Power and Privacy Budgetscited by 1
  4. 2022Random Orthogonalization for Federated Learning in Massive MIMO Systemscited by 0