论文标题
FedGia：一种用于联合学习的高效混合算法
FedGiA: An Efficient Hybrid Algorithm for Federated Learning
论文作者
论文摘要
Federated学习最近显示了其进步，但仍面临许多挑战，例如算法如何节省通信资源并降低计算成本以及它们是否融合。为了解决这些关键问题，我们提出了一种混合联合学习算法（FEDGIA），该算法结合了梯度下降和乘数不确定的交替方向方法。所提出的算法比从理论上和数值上的几种最先进的算法要高的通信和计算效率。此外，它还在轻度条件下全球收敛。
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.
