Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning

Constantin Philippenko, Aymeric Dieuleveut

In this paper, we investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning. We un…