基于外推自适应步长的压缩联邦学习优化算法

Extrapolation Adaptive Step Size for Compressed Federated Learning Optimization Algorithm

摘要: 由于联邦学习中的通信成本高昂，依赖通信压缩的方法正日益受到关注。本文基于外推自适应步长和压缩联邦学习框架提出了外推压缩联邦学习算法(ExpFedCom)。ExpFedCom算法支持通信压缩并在凸、强凸以及非凸条件下实现了快速收敛。数值实验表明ExpFedCom算法既实现了快速收敛又节省了通信成本，这在实际生活中有着较好的应用价值。

Abstract: The high communication costs in federated learning have motivated increasing interest in communication compression methods. This paper proposes Extrapolated Federated Compression (ExpFedCom), a novel algorithm that integrates extrapolation adaptive step sizes with a communication compression federated learning framework. The ExpFedCom algorithm supports communication compression while achieving faster convergence rates under convex, strongly convex, and nonconvex settings. Numerical experiments demonstrate that ExpFedCom simultaneously attains faster convergence and significant communication savings, exhibiting strong practical potential for real-world applications.