# Faster Rates for Compressed Federated Learning with Client-Variance Reduction

The paper Faster Rates for Compressed Federated Learning with Client-Variance Reduction was accepted by the SIAM Journal (SIMODS).

The paper *“Faster Rates for Compressed Federated Learning with Client-Variance Reduction”* was accepted by the SIAM Journal on Mathematics of Data Science(SIMODS). The SIAM Journal on Mathematics of Data Science publishes work that advances mathematical, statistical, and computational methods in the context of data and information sciences.

I was glad to work with my peers and hope for further cooperation:

- Haoyu Zhao from Princeton University
- Zhize Li from Carnegie Mellon University
- Prof. Peter Richtarik from King Abdullah University of Science and Technology

The arXiv version of our paper has been updated on 24 September 2023: https://arxiv.org/abs/2112.13097

Written on September 26, 2023