We propose a new training algorithm, named DualFL (Dualized Federated Learning), for solving distributed optimization problems in federated learning. DualFL achieves communication acceleration for very general convex cost functions, thereby providing a solution to an open theoretical problem in federated learning concerning cost functions that may not be smooth nor strongly convex. We provide a detailed analysis for the local iteration complexity of DualFL to ensure the overall computational efficiency of DualFL. Furthermore, we introduce a completely new approach for the convergence analysis of federated learning based on a dual formulation. This new technique enables concise and elegant analysis, which contrasts the complex calculations used in existing literature on convergence of federated learning algorithms.

翻译：我们提出了一种名为DualFL（对偶联邦学习）的新型训练算法，用于解决联邦学习中的分布式优化问题。DualFL针对非常一般的凸代价函数实现了通信加速，从而为联邦学习中关于可能非光滑且非强凸代价函数的一个开放性理论问题提供了解决方案。我们对DualFL的局部迭代复杂度进行了详细分析，以确保其整体计算效率。此外，我们提出了一种基于对偶形式化的全新联邦学习收敛性分析方法。这一新方法使得分析过程简洁而优雅，与现有联邦学习算法收敛性文献中使用的复杂计算形成了鲜明对比。