Federated Learning via Inexact ADMM

Shenglong Zhou; Geoffrey Ye Li

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arXiv · 2204.10607

Federated Learning via Inexact ADMM

Shenglong Zhou·Geoffrey Ye Li

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Abstract

One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, in this paper, we develop an inexact alternating direction method of multipliers (ADMM), which is both computation- and communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions. Furthermore, it has a high numerical performance compared with several state-of-the-art algorithms for federated learning.

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BibTeX RIS

Shenglong Zhou, Geoffrey Ye Li. 2022-04-22. Federated Learning via Inexact ADMM. https://arxiv.org/abs/2204.10607

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