  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.
