Federated Frank-Wolfe Algorithm
Ali Dadras, Sourasekhar Banerjee, Karthik Prakhya, Alp Yurtsever

TL;DR
The paper introduces FedFW, a federated learning algorithm using Frank-Wolfe that efficiently handles constrained problems with privacy, low cost, and sparse communication, suitable for both convex and non-convex objectives.
Contribution
It proposes FedFW, a novel federated Frank-Wolfe algorithm that reduces projection costs and supports both deterministic and stochastic settings for constrained machine learning.
Findings
FedFW achieves $ ext{ extsterling}$-suboptimal solutions within $O( ext{ extsterling}^{-2})$ iterations for convex objectives.
FedFW converges within $O( ext{ extsterling}^{-3})$ iterations for non-convex objectives.
Empirical results demonstrate FedFW's effectiveness on various machine learning tasks.
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 -suboptimal solution within iterations for smooth and convex objectives, and iterations for smooth but non-convex objectives. Furthermore, we present a stochastic variant of FedFW and show that it finds a solution within iterations in the convex setting. We demonstrate the empirical performance of FedFW on several machine learning tasks.
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Taxonomy
Topicsgraph theory and CDMA systems · Cellular Automata and Applications · DNA and Biological Computing
MethodsSoftmax · Attention Is All You Need
