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arXiv · 2503.21627

Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning

Karlo Palenzuela·Ali Dadras·Alp Yurtsever·Tommy Löfstedt
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Abstract

Multiple local steps are key to communication-efficient federated learning. However, theoretical guarantees for such algorithms, without data heterogeneity-bounding assumptions, have been lacking in general non-smooth convex problems. Leveraging projection-efficient optimization methods, we propose FedMLS, a federated learning algorithm with provable improvements from multiple local steps. FedMLS attains an $\epsilon$-suboptimal solution in $\mathcal{O}(1/\epsilon)$ communication rounds, requiring a total of $\mathcal{O}(1/\epsilon^2)$ stochastic subgradient oracle calls.

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Karlo Palenzuela, Ali Dadras, Alp Yurtsever, Tommy Löfstedt. 2025-03-27. Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning. https://arxiv.org/abs/2503.21627

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