  In the landscape of generative artificial intelligence, diffusion-based
models present challenges for socio-technical systems in data requirements and
privacy. Traditional approaches like federated learning distribute the learning
process but strain individual clients, especially with constrained resources
(e.g., edge devices). In response to these challenges, we introduce CollaFuse,
a novel framework inspired by split learning. Tailored for efficient and
collaborative use of denoising diffusion probabilistic models, CollaFuse
enables shared server training and inference, alleviating client computational
burdens. This is achieved by retaining data and computationally inexpensive GPU
processes locally at each client while outsourcing the computationally
expensive processes to the shared server. Demonstrated in a healthcare context,
CollaFuse enhances privacy by highly reducing the need for sensitive
information sharing. These capabilities hold the potential to impact various
application areas, such as the design of edge computing solutions, healthcare
research, or autonomous driving. In essence, our work advances distributed
machine learning, shaping the future of collaborative GenAI networks.
