Phoenix: A Federated Generative Diffusion Model

Understand

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.