Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity

1 code implementation • 23 Jun 2023 • Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich

In this paper, we establish a precise and quantifiable correspondence between data heterogeneity and parameters in the convergence rate when a fraction of data is shuffled across clients.

Federated Learning