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Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection

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arxiv 2201.05762 v2 pith:ANTVN3T3 submitted 2022-01-15 cs.LG cs.DC

classification cs.LGcs.DC
keywords selectionvariancebetterclientheterogeneousschemeaccuracyconvergence
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Client selection strategies are widely adopted to handle the communication-efficient problem in recent studies of Federated Learning (FL). However, due to the large variance of the selected subset's update, prior selection approaches with a limited sampling ratio cannot perform well on convergence and accuracy in heterogeneous FL. To address this problem, in this paper, we propose a novel stratified client selection scheme to reduce the variance for the pursuit of better convergence and higher accuracy. Specifically, to mitigate the impact of heterogeneity, we develop stratification based on clients' local data distribution to derive approximate homogeneous strata for better selection in each stratum. Concentrating on a limited sampling ratio scenario, we next present an optimized sample size allocation scheme by considering the diversity of stratum's variability, with the promise of further variance reduction. Theoretically, we elaborate the explicit relation among different selection schemes with regard to variance, under heterogeneous settings, we demonstrate the effectiveness of our selection scheme. Experimental results confirm that our approach not only allows for better performance relative to state-of-the-art methods but also is compatible with prevalent FL algorithms.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review

    cs.LG 2025-06 conditional novelty 4.0 of 10

    A survey categorizing and comparing twelve federated learning methods for partial client participation, weakened by several citation mismatches and unsourced benchmark numbers.

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