Federated Frank-Wolfe Algorithm

Code

• github.com/sourasb05/Federated-Frank-Wolfe
  Officialpytorch★ 1

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 O(^-2) iterations for smooth and convex objectives, and O(^-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(^-3) iterations in the convex setting. We demonstrate the empirical performance of FedFW on several machine learning tasks.

Tasks

Reproductions