A Stochastic Gradient Approach for Communication Efficient Confederated Learning

Bin Wang; Jun Fang; Hongbin Li; Yonina C. Eldar

ICASSP 2024accepted0 citations

A Stochastic Gradient Approach for Communication Efficient Confederated Learning

Bin Wang, Jun Fang, Hongbin Li, Yonina C. Eldar

Abstract

In this work, we consider a multi-server federated learning (FL) framework, referred to as Confederated Learning (CFL), in order to accommodate a larger number of users. To reduce the communication overhead of the CFL system, we propose a linearly convergent stochastic gradient method. The proposed algorithm incorporates a conditionally-triggered user selection (CTUS) mechanism as the central component. Simulation results show that it achieves advantageous communication efficiency over GT-SAGA.

BibTeX

@inproceedings{icassp2024_astochasticgradi,
  title = {A Stochastic Gradient Approach for Communication Efficient Confederated Learning},
  author = {Bin Wang and Jun Fang and Hongbin Li and Yonina C. Eldar},
  booktitle = {ICASSP 2024},
  year = {2024}
}