One-Shot Federated Learning with Classifier-Free Diffusion Models

• URL: http://arxiv.org/abs/2502.08488v1
• Date: Wed, 12 Feb 2025 15:23:29 GMT
• Title: One-Shot Federated Learning with Classifier-Free Diffusion Models
• Authors: Obaidullah Zaland, Shutong Jin, Florian T. Pokorny, Monowar Bhuyan,
• Abstract summary: One-shot federated learning (OSFL) addresses this by forming a global model with a single communication round. OSCAR is a simple yet cost-effective OSFL approach that outperforms the state-of-the-art on four datasets while reducing the communication load by at least 99%.
• Score: 7.338353383261602
• License:
• 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 - often 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, seamlessly integrated into a classifier-free diffusion model pipeline for server-side data generation. OSCAR is a simple yet cost-effective OSFL approach that outperforms the state-of-the-art on four benchmarking datasets while reducing the communication load by at least 99%.

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