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.

翻译：在生成式人工智能领域，基于扩散的模型在数据需求和隐私方面对社会技术系统提出了挑战。诸如联邦学习等传统方法虽能分散学习过程，但会加剧个体客户端的负担，尤其是在资源受限（例如边缘设备）的情况下。为应对这些挑战，我们提出了CollaFuse——一种受分割学习启发的新型框架。该框架专为高效协作使用去噪扩散概率模型而设计，支持共享服务器上的训练与推理，从而减轻客户端的计算负担。其核心机制是：每个客户端本地保留数据并执行计算开销较低的GPU进程，而将计算密集型任务外包给共享服务器。在医疗场景中的验证表明，CollaFuse通过大幅减少敏感信息共享需求来增强隐私保护。这些能力有望影响边缘计算解决方案设计、医疗研究或自动驾驶等多个应用领域。本质上，我们的工作推动了分布式机器学习发展，塑造了协作式生成式AI网络的未来。