Paper ID: 2205.15580

A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting

Alexander Tyurin, Peter Richtárik

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.

Submitted: May 31, 2022