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We propose a new training algorithm, named DualFL (Dualized FederatedLearning), for solving distributed optimization problems in federated learning.DualFL achieves communication acceleration for very general convex costfunctions, thereby providing a solution to an open theoretical problem infederated learning concerning cost functions that may not be smooth norstrongly convex. We provide a detailed analysis for the local iterationcomplexity of DualFL to ensure the overall computational efficiency of DualFL.Furthermore, we introduce a completely new approach for the convergenceanalysis of federated learning based on a dual formulation. This new techniqueenables concise and elegant analysis, which contrasts the complex calculationsused in existing literature on convergence of federated learning algorithms.

DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime