Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models

arXiv:2405.01494v1 Announce Type: cross
Abstract: Federated learning (FL) enables multiple clients to train models collectively while preserving data privacy. However, FL faces challenges in terms of communication cost and data heterogeneity. One-shot federated learning has emerged as a solution by reducing communication rounds, improving efficiency, and providing better security against eavesdropping attacks. Nevertheless, data heterogeneity remains a significant challenge, impacting performance. This work explores the effectiveness of diffusion models in one-shot FL, demonstrating their applicability in addressing data heterogeneity and …

abstract arxiv challenges communication cost cs.cr cs.cv cs.lg data data privacy diffusion diffusion models efficiency federated learning however improving multiple privacy security solution terms train type while