用无分类器扩散模型实现单轮联邦学习，大幅降低通信开销。
One-Shot Federated Learning with Classifier-Free Diffusion Models
- 客户端用基础模型生成类别数据表征，融入无分类器扩散流程。
- 在四个数据集上超越当前最佳，通信量减少至少99%。
- 无需额外训练分类器，适合资源受限的边缘设备应用。
联邦学习（FL）可在不集中数据的情况下协同训练，但需多次客户端与服务器通信，带来高昂通信成本。单轮联邦学习（OSFL）通过仅一次通信构建全局模型，通常依赖服务器端模型蒸馏或辅助数据生成，多基于预训练扩散模型（DMs）。然而现有基于扩散模型的OSFL方法普遍采用有分类器引导的扩散模型，需在每个客户端训练辅助分类器，增加计算开销。本文提出OSCAR（One-Shot Federated Learning with Classifier-Free Diffusion Models），一种新OSFL方法，无需辅助模型。OSCAR利用基础模型在客户端生成类别特定的数据表征，并将其整合进无分类器扩散模型流程中，用于服务器端数据生成。实验表明，OSCAR在四个基准数据集上均优于当前最佳方法，且通信负载降低至少99%。
原文摘要 · Abstract (English)
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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