Exploring the Effectiveness of Diffusion Models in One-Shot Federated Learning
Keywords: Federated Learning, Diffusion Models
TL;DR: This paper explores diffusion models for one-shot federated learning to improve the performance of the process while addressing communication cost, data heterogeneity, and privacy concerns
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 improving FL performance. Additionally, we investigate the utility of our diffusion model approach, FedDiff, compared to other one-shot FL methods under differential privacy (DP). Furthermore, to improve generated sample quality under DP settings, we propose a simple Fourier Magnitude Filtering (FMF) method, enhancing the effectiveness of the generated data for global model training. Code will be made publicly available.
Primary Area: societal considerations including fairness, safety, privacy
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Submission Number: 2100
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