mlsanityData to 7 Oct 2026

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks

arXiv 2501.11267 ↗First posted 20 Jan 2025cs.DC cs.LGcited by 2

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Abstract (arXiv)

Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead caused by frequent and costly server-device synchronization. Notably, most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance resulting from the prevalent issue of device heterogeneity. This variance severely decelerates algorithm convergence, increasing communication overhead and making it more challenging to achieve a well-performed model. In this paper, we propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme to achieve heterogeneity-robustness in the presence of quantized transmission and heterogeneous local updates among active edge devices. Comprehensive theoretical analysis justifies that FedQVR is inherently resilient to device heterogeneity and has a comparable convergence rate even with a small number of quantization bits, yielding significant communication savings. Besides, considering non-ideal wireless channels, we propose FedQVR-E which enhances the convergence of FedQVR by performing joint allocation of bandwidth and quantization bits across devices under constrained transmission delays. Extensive experimental results are also presented to demonstrate the superior performance of the proposed algorithms over their counterparts in terms of both communication efficiency and application performance.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 37 of its 47 references that are in the map. First posted on arXiv 20 Jan 2025.

Builds on

37 of 47 references.

References · 37

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25 more
  1. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 152
  2. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 143
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  18. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  19. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  20. 2024FedFisher: Leveraging Fisher Information for One-Shot Federated Learningcited by 15
  21. 2022Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantizationcited by 14
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignmentcited by 0
  2. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0