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Distributionally Robust Federated Learning: An ADMM Algorithm

arXiv 2503.18436 ↗First posted 24 Mar 2025cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) aims to train machine learning (ML) models collaboratively using decentralized data, bypassing the need for centralized data aggregation. Standard FL models often assume that all data come from the same unknown distribution. However, in practical situations, decentralized data frequently exhibit heterogeneity. We propose a novel FL model, Distributionally Robust Federated Learning (DRFL), that applies distributionally robust optimization to overcome the challenges posed by data heterogeneity and distributional ambiguity. We derive a tractable reformulation for DRFL and develop a novel solution method based on the alternating direction method of multipliers (ADMM) algorithm to solve this problem. Our experimental results demonstrate that DRFL outperforms standard FL models under data heterogeneity and ambiguity.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 44 of its 63 references that are in the map. First posted on arXiv 24 Mar 2025.

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References · 44

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32 more
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