Exploring the potential of federated learning for diffusion model: Training and fine-tuning

Diffusion models, a state-of-the-art generative model, have drawn attention for their capacity to produce high-quality, divers, and flexible content. However, the training of these models typically necessitates large datasets, a task that can be hindered by challenges related to privacy concerns and data distribution constraints. Due to the amount of data and hardware required for large model training, all centralized training will be done by large companies or labs with computing power. Federated Learning provides a decentralized method that allows for model training across several data sources while maintaining the data's localization, reducing privacy threats. This research proposes and evaluate a novel approach for utilizing Federated Learning in the context of diffusion models. This paper investigates the feasibility of training and fine-tuning diffusion models in a federated setting, considering various data distributions and privacy constraints. This study used the Federated Averaging (FedAvg) technique to train the unconditional diffusion model as well as to fine-tune the pre-trained diffusion mode. The experimental results demonstrate that federated training of diffusion models can achieve comparable performance to centralized training methods while preserving data locality. Additionally, Federated Learning can be effectively applied to fine-tune pre-trained diffusion model, enabling adaptation to specific tasks without exposing sensitive data. Overall, this work demonstrates Federated Learning's potential as a useful tool for training and fine-tuning diffusion models in a privacy-preserving manner.