Generative AI has made impressive strides in enabling users to create diverse and realistic visual content such as images, videos, and audio. However, training generative models on large centralized datasets can pose challenges in terms of data privacy, security, and accessibility. Federated learning (FL) is an approach that uses decentralized techniques to collaboratively train a shared deep learning model while retaining the training data on individual edge devices to preserve data privacy. This paper proposes a novel method for training a Denoising Diffusion Probabilistic Model (DDPM) across multiple data sources using FL techniques. Diffusion models, a newly emerging generative model, show promising results in achieving superior quality images than Generative Adversarial Networks (GANs). Our proposed method Phoenix is an unconditional diffusion model that leverages strategies to improve the data diversity of generated samples even when trained on data with statistical heterogeneity or Non-IID (Non-Independent and Identically Distributed) data. We demonstrate how our approach outperforms the default diffusion model in an FL setting. These results indicate that high-quality samples can be generated by maintaining data diversity, preserving privacy, and reducing communication between data sources, offering exciting new possibilities in the field of generative AI.

翻译：生成式人工智能在帮助用户创建图像、视频和音频等多样化且逼真的视觉内容方面取得了显著进展。然而，在大型集中式数据集上训练生成模型可能面临数据隐私、安全性和可访问性方面的挑战。联邦学习（FL）是一种利用去中心化技术协作训练共享深度学习模型的方法，同时将训练数据保留在单个边缘设备上以保护数据隐私。本文提出了一种新颖方法，即使用FL技术跨多个数据源训练去噪扩散概率模型（DDPM）。扩散模型作为一种新兴的生成模型，在生成比生成对抗网络（GANs）更高质量的图像方面展现出令人瞩目的成果。我们提出的方法Phoenix是一种无条件的扩散模型，即使在使用具有统计异质性或非独立同分布（Non-IID）数据训练时，也能通过策略提升生成样本的数据多样性。我们展示了该方法在FL环境下优于默认扩散模型的表现。这些结果表明，通过维护数据多样性、保护隐私以及减少数据源之间的通信，能够生成高质量样本，为生成式人工智能领域开辟了激动人心的新可能性。