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arXiv · 2408.10090

Federated Frank-Wolfe Algorithm

Ali Dadras·Sourasekhar Banerjee·Karthik Prakhya·Alp Yurtsever
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Abstract

Federated learning (FL) has gained a lot of attention in recent years for building privacy-preserving collaborative learning systems. However, FL algorithms for constrained machine learning problems are still limited, particularly when the projection step is costly. To this end, we propose a Federated Frank-Wolfe Algorithm (FedFW). FedFW features data privacy, low per-iteration cost, and communication of sparse signals. In the deterministic setting, FedFW achieves an $\varepsilon$-suboptimal solution within $O(\varepsilon^{-2})$ iterations for smooth and convex objectives, and $O(\varepsilon^{-3})$ iterations for smooth but non-convex objectives. Furthermore, we present a stochastic variant of FedFW and show that it finds a solution within $O(\varepsilon^{-3})$ iterations in the convex setting. We demonstrate the empirical performance of FedFW on several machine learning tasks.

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Ali Dadras, Sourasekhar Banerjee, Karthik Prakhya, Alp Yurtsever. 2024-08-19. Federated Frank-Wolfe Algorithm. https://arxiv.org/abs/2408.10090

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