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arXiv · 2508.06692

HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning

Md. Akmol Masud·Md Abrar Jahin·Mahmud Hasan
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Abstract

Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. However, under non-IID data, these signals often decorrelate or invert. A bandwidth-driven allocator then risks compressing the most informative gradients hardest. We propose HeteRo-Select, a framework that replaces bandwidth with a per-client informativeness score as the primary driver of compression. The score jointly governs three decisions per round: client selection, compression ratio, and server aggregation weight, with bandwidth retained only as a hard ceiling. Score-proportional selection provably reduces the effective heterogeneity of the chosen subset; score-proportional compression provably lowers aggregate top-$k$ error at fixed traffic. Under the exact FedCG simulation protocol, HeteRo-Select delivers a $1.78\times$ speedup and an $18.2\%$ reduction in traffic on CIFAR-10. The same configuration, unchanged, scales from a $7{,}850$-parameter logistic regression to an $11.27$M-parameter ResNet-18, hitting the accuracy target on three of four benchmarks. When bandwidth and informativeness are deliberately anti-correlated, the method still achieves the target accuracy with less traffic than the normal-bandwidth run.

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BibTeXRIS

Md. Akmol Masud, Md Abrar Jahin, Mahmud Hasan. 2025-08-08. HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning. https://arxiv.org/abs/2508.06692

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