Phoenix: A Federated Generative Diffusion Model
Fiona Victoria Stanley Jothiraj Afra Mashhadi
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.