Paper Number
1767
Paper Type
Short Paper
Abstract
In the landscape of generative artificial intelligence, diffusion 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. In response to these challenges, we introduce \textsc{CollaFuse}, a novel framework inspired by split learning. Tailored for efficient and collaborative use of denoising diffusion probabilistic models, \textsc{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, \textsc{CollaFuse} enhances privacy by reducing the need for sensitive information sharing. These capabilities hold the potential to impact various application areas, such as edge computing, healthcare research, or autonomous driving. In essence, our work advances distributed machine learning, shaping the future of collaborative GenAI networks.
Recommended Citation
Zipperling, Domenique; Allmendinger, Simeon; Struppek, Lukas; and Kühl, Niklas, "Collafuse: Navigating Limited Resources and Privacy in Collaborative Generative AI" (2024). ECIS 2024 Proceedings. 1.
https://aisel.aisnet.org/ecis2024/track07_busanalytics/track07_busanalytics/1
Collafuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
In the landscape of generative artificial intelligence, diffusion 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. In response to these challenges, we introduce \textsc{CollaFuse}, a novel framework inspired by split learning. Tailored for efficient and collaborative use of denoising diffusion probabilistic models, \textsc{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, \textsc{CollaFuse} enhances privacy by reducing the need for sensitive information sharing. These capabilities hold the potential to impact various application areas, such as edge computing, healthcare research, or autonomous driving. In essence, our work advances distributed machine learning, shaping the future of collaborative GenAI networks.
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