FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
We develop two new algorithms, called, FedDR and asyncFedDR, for solving a fundamental nonconvex composite optimization problem in federated learning. Our algorithms rely on a novel combination between a nonconvex Douglas-Rachford splitting method, randomized block-coordinate strategies, and asynchr…