Diffusion models have shown great potential for vision-related tasks, particularly for image generation. However, their training is typically conducted in a centralized manner, relying on data collected from publicly available sources. This approach may not be feasible or practical in many domains, such as the medical field, which involves privacy concerns over data collection. Despite the challenges associated with privacy-sensitive data, such domains could still benefit from valuable vision services provided by diffusion models. Federated learning (FL) plays a crucial role in enabling decentralized model training without compromising data privacy. Instead of collecting data, an FL system gathers model parameters, effectively safeguarding the private data of different parties involved. This makes FL systems vital for managing decentralized learning tasks, especially in scenarios where privacy-sensitive data is distributed across a network of clients. Nonetheless, FL presents its own set of challenges due to its distributed nature and privacy-preserving properties. Therefore, in this study, we explore the FL strategy to train diffusion models, paving the way for the development of federated diffusion models. We conduct experiments on various FL scenarios, and our findings demonstrate that federated diffusion models have great potential to deliver vision services to privacy-sensitive domains.

翻译：扩散模型在视觉相关任务（特别是图像生成）中展现出巨大潜力。然而，其训练通常在集中式框架下进行，依赖公开来源收集的数据。在医疗等涉及数据采集隐私问题的领域，这种训练方式可能不具备可行性或实用性。尽管隐私敏感数据存在诸多挑战，但这些领域仍可能受益于扩散模型提供的卓越视觉服务。联邦学习（FL）能在不损害数据隐私的前提下实现去中心化模型训练，其通过收集模型参数而非原始数据来有效保护参与各方的私有数据。这一特性使得FL系统对管理分布式学习任务至关重要，尤其适用于隐私敏感数据分布在客户端网络中的场景。然而，FL因其分布式特性和隐私保护机制也面临独特挑战。为此，本研究探索采用FL策略训练扩散模型，为联邦扩散模型的开发铺平道路。我们在多种FL场景下开展实验，结果表明联邦扩散模型在向隐私敏感领域提供视觉服务方面具有巨大潜力。