Federated Frank-Wolfe Algorithm
<|reference_start|>Federated Frank-Wolfe Algorithm: 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.<|reference_end|>
@article{dadras2024federated,
    title={Federated Frank-Wolfe Algorithm},
    author={Ali Dadras, Sourasekhar Banerjee, Karthik Prakhya, Alp Yurtsever},
    journal={arXiv preprint arXiv:2408.10090},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10090},
    primaryClass={cs.LG cs.DC}
}