We propose a novel training algorithm called DualFL (Dualized Federated
Learning), for solving a distributed optimization problem in federated
learning. Our approach is based on a specific dual formulation of the federated
learning problem. DualFL achieves communication acceleration under various
settings on smoothness and strong convexity of the problem. Moreover, it
theoretically guarantees the use of inexact local solvers, preserving its
optimal communication complexity even with inexact local solutions. DualFL is
the first federated learning algorithm that achieves communication
acceleration, even when the cost function is either nonsmooth or non-strongly
convex. Numerical results demonstrate that the practical performance of DualFL
is comparable to those of state-of-the-art federated learning algorithms, and
it is robust with respect to hyperparameter tuning.

本文提出了一种名为 DualFL 的新型训练算法，可以解决联邦学习中分布式优化问题，并实现了在不同条件下的通信加速，在使用不精确本地解算器时也保持其最佳的通信复杂度。此外，这是第一个即使在成本函数为非平滑或非强凸时也能实现通信加速的联邦学习算法。