We present a new method that includes three key components of distributed optimization and federated learning: variance reduction of stochastic gradients, partial participation, and compressed communication. We prove that the new method has optimal oracle complexity and state-of-the-art communication complexity in the partial participation setting. Regardless of the communication compression feature, our method successfully combines variance reduction and partial participation: we get the optimal oracle complexity, never need the participation of all nodes, and do not require the bounded gradients (dissimilarity) assumption.
@article{arxiv.2205.15580,
title = {A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting},
author = {Alexander Tyurin and Peter Richtárik},
journal= {arXiv preprint arXiv:2205.15580},
year = {2024}
}