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

arXiv:2306.13263v2 Announce Type: replace
Abstract: In federated learning, data heterogeneity is a critical challenge. A straightforward solution is to shuffle the clients' data to homogenize the distribution. However, this may violate data access rights, and how and when shuffling can accelerate the convergence of a federated optimization algorithm is not theoretically well understood. 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 …

abstract algorithm arxiv challenge convergence cs.cv cs.dc cs.lg data data access distribution federated learning however optimization rights solution synthetic synthetic data type