Personalized Federated Fine-Tuning of Vision Foundation Models for Healthcare

• URL: http://arxiv.org/abs/2510.12741v1
• Date: Tue, 14 Oct 2025 17:18:12 GMT
• Title: Personalized Federated Fine-Tuning of Vision Foundation Models for Healthcare
• Authors: Adam Tupper, Christian Gagné,
• Abstract summary: Foundation models open up new possibilities for the use of AI in healthcare.<n>We propose a new personalized federated fine-tuning method that learns LoRA adapters to disentangle general and client-specific knowledge.
• Score: 5.142160533428574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models open up new possibilities for the use of AI in healthcare. However, even when pre-trained on health data, they still need to be fine-tuned for specific downstream tasks. Furthermore, although foundation models reduce the amount of training data required to achieve good performance, obtaining sufficient data is still a challenge. This is due, in part, to restrictions on sharing and aggregating data from different sources to protect patients' privacy. One possible solution to this is to fine-tune foundation models via federated learning across multiple participating clients (i.e., hospitals, clinics, etc.). In this work, we propose a new personalized federated fine-tuning method that learns orthogonal LoRA adapters to disentangle general and client-specific knowledge, enabling each client to fully exploit both their own data and the data of others. Our preliminary results on real-world federated medical imaging tasks demonstrate that our approach is competitive against current federated fine-tuning methods.

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