Phoenix: A Federated Generative Diffusion Model

TL;DR

Phoenix introduces a federated diffusion model that enables high-quality, diverse image generation across multiple decentralized data sources while preserving privacy and reducing communication costs.

Contribution

This paper presents a novel federated training method for diffusion models, improving data diversity and quality in non-IID settings compared to existing approaches.

Findings

Outperforms standard diffusion models in federated settings

Maintains high data diversity despite data heterogeneity

Reduces communication overhead in federated training

Abstract

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.