Federated Frank-Wolfe Algorithm
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.
Cite
Text
Dadras et al. "Federated Frank-Wolfe Algorithm." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024. doi:10.1007/978-3-031-70352-2_4Markdown
[Dadras et al. "Federated Frank-Wolfe Algorithm." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024.](https://mlanthology.org/ecmlpkdd/2024/dadras2024ecmlpkdd-federated/) doi:10.1007/978-3-031-70352-2_4BibTeX
@inproceedings{dadras2024ecmlpkdd-federated,
title = {{Federated Frank-Wolfe Algorithm}},
author = {Dadras, Ali and Banerjee, Sourasekhar and Prakhya, Karthik and Yurtsever, Alp},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2024},
pages = {58-75},
doi = {10.1007/978-3-031-70352-2_4},
url = {https://mlanthology.org/ecmlpkdd/2024/dadras2024ecmlpkdd-federated/}
}