Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whether they converge. To address these critical issues, we propose a hybrid federated learning algorithm (FedGiA) that combines the gradient descent and the inexact alternating direction method of multipliers. The proposed algorithm is more communication- and computation-efficient than several state-of-the-art algorithms theoretically and numerically. Moreover, it also converges globally under mild conditions.

翻译：联邦学习近年来展现出其优势，但仍面临诸多挑战，例如算法如何节省通信资源、降低计算成本以及是否能实现收敛。为解决这些关键问题，我们提出一种结合梯度下降与不精确交替方向乘子法的混合联邦学习算法（FedGiA）。该算法在理论上和数值实验中都相比几种最先进的算法在通信与计算效率上更优。此外，它在温和条件下也能实现全局收敛。