FedBoost: A Communication-Efficient Algorithm for Federated Learning

This work provides communication-efficient ensemble algorithms for federated learning, where per-round communication cost is independent of the size of the ensemble, and proves the optimality of ensemble methods for density estimation for standard empirical risk minimization and agnostic risk minimizations.