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arXiv · 2502.08488

One-Shot Federated Learning with Classifier-Free Diffusion Models

Obaidullah Zaland·Shutong Jin·Florian T. Pokorny·Monowar Bhuyan
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Abstract

Federated learning (FL) enables collaborative learning without data centralization but introduces significant communication costs due to multiple communication rounds between clients and the server. One-shot federated learning (OSFL) addresses this by forming a global model with a single communication round, often relying on the server's model distillation or auxiliary dataset generation - mostly through pre-trained diffusion models (DMs). Existing DM-assisted OSFL methods, however, typically employ classifier-guided DMs, which require training auxiliary classifier models at each client, introducing additional computation overhead. This work introduces OSCAR (One-Shot Federated Learning with Classifier-Free Diffusion Models), a novel OSFL approach that eliminates the need for auxiliary models. OSCAR uses foundation models to devise category-specific data representations at each client which are integrated into a classifier-free diffusion model pipeline for server-side data generation. In our experiments, OSCAR outperforms the state-of-the-art on four benchmark datasets while reducing the communication load by at least 99%.

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Obaidullah Zaland, Shutong Jin, Florian T. Pokorny, Monowar Bhuyan. 2025-02-12. One-Shot Federated Learning with Classifier-Free Diffusion Models. https://doi.org/10.1109/icme59968.2025.11209111

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