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

arXiv: https://arxiv.org/abs/2205.15580 · HTML: https://mlsanity.com/paper/2205.15580/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2022.

## Built on by (most cited first, 1 of 1)

- [Tighter Performance Theory of FedExProx](https://mlsanity.com/paper/2410.15368/) (2024)

## Abstract (arXiv)

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.