In the landscape of generative artificial intelligence, diffusion-based
models have emerged as a promising method for generating synthetic images.
However, the application of diffusion models poses numerous challenges,
particularly concerning data availability, computational requirements, and
privacy. Traditional approaches to address these shortcomings, like federated
learning, often impose significant computational burdens on individual clients,
especially those with constrained resources. In response to these challenges,
we introduce a novel approach for distributed collaborative diffusion models
inspired by split learning. Our approach facilitates collaborative training of
diffusion models while alleviating client computational burdens during image
synthesis. This reduced computational burden is achieved by retaining data and
computationally inexpensive processes locally at each client while outsourcing
the computationally expensive processes to shared, more efficient server
resources. Through experiments on the common CelebA dataset, our approach
demonstrates enhanced privacy by reducing the necessity for sharing raw data.
These capabilities hold significant potential across various application areas,
including the design of edge computing solutions. Thus, our work advances
distributed machine learning by contributing to the evolution of collaborative
diffusion models.

通过分布式协作扩散模型，我们提出了一种新的方法，在感知图像的同时减轻了客户端的计算负担，从而实现协作训练和数据隐私的增强。