mlsanityData to 7 Oct 2026

Noise-Robust and Resource-Efficient ADMM-based Federated Learning

arXiv 2409.13451 ↗First posted 20 Sep 2024cs.LG cs.DC eess.SPcited by 1

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Abstract (arXiv)

Federated learning (FL) leverages client-server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To address this problem, we propose a novel FL algorithm that enhances robustness against communication noise while also reducing communication load. We derive the proposed algorithm through solving the weighted least-squares (WLS) regression problem as an illustrative example. We first frame WLS regression as a distributed convex optimization problem over a federated network employing random scheduling for improved communication efficiency. We then apply the alternating direction method of multipliers (ADMM) to iteratively solve this problem. To counteract the detrimental effects of cumulative communication noise, we introduce a key modification by eliminating the dual variable and implementing a new local model update at each participating client. This subtle yet effective change results in using a single noisy global model update at each client instead of two, improving robustness against additive communication noise. Furthermore, we incorporate another modification enabling clients to continue local updates even when not selected by the server, leading to substantial performance improvements. Our theoretical analysis confirms the convergence of our algorithm in both mean and the mean-square senses, even when the server communicates with a random subset of clients over noisy links at each iteration. Numerical results validate the effectiveness of our proposed algorithm and corroborate our theoretical findings.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 64 references that are in the map. First posted on arXiv 20 Sep 2024.

Builds on

31 of 64 references.

References · 31

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19 more
  1. 2021EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learningcited by 53
  2. 2021Federated Learning: A Signal Processing Perspectivecited by 49
  3. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  4. 2022Federated Learning via Inexact ADMMcited by 44
  5. 2019Robust Federated Learning with Noisy Communicationcited by 33
  6. 2018Federated Learning for Ultra-Reliable Low-Latency V2V Communicationscited by 30
  7. 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
  8. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  9. 2021Communication-Efficient Federated Learning via Quantized Compressed Sensingcited by 17
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  11. 2022Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Frameworkcited by 9
  12. 2021Federated Learning over Noisy Channels: Convergence Analysis and Design Examplescited by 8
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  14. 2023Personalized Graph Federated Learning with Differential Privacycited by 4
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  16. 2024Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Designcited by 3
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  18. 2015On the Asymptotic Bias of the Diffusion-Based Distributed Pareto Optimizationcited by 1
  19. 2024Distributed Maximum Consensus over Noisy Linkscited by 1

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Citing papers per year: 2026: 1.

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  1. 2026Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharingcited by 0