Motivated by federated learning, we consider the hub-and-spoke model of
distributed optimization in which a central authority coordinates the
computation of a solution among many agents while limiting communication. We
first study some past procedures for federated optimization, and show that
their fixed points need not correspond to stationary points of the original
optimization problem, even in simple convex settings with deterministic
updates. In order to remedy these issues, we introduce FedSplit, a class of
algorithms based on operator splitting procedures for solving distributed
convex minimization with additive structure. We prove that these procedures
have the correct fixed points, corresponding to optima of the original
optimization problem, and we characterize their convergence rates under
different settings. Our theory shows that these methods are provably robust to
inexact computation of intermediate local quantities. We complement our theory
with some simple experiments that demonstrate the benefits of our methods in
practice.

本文介绍了一类名为 FedSplit 的基于操作分裂的算法，用于解决具有加性结构的分布式凸最小化，该算法在联邦学习、分布式优化等领域有较好的应用前景。