面向医疗保健的视觉基础模型个性化联邦微调
Personalized Federated Fine-Tuning of Vision Foundation Models for Healthcare
AI总结：
本文提出一种通过正交LoRA适配器解耦通用与客户端特定知识的个性化联邦微调方法，用于医疗视觉基础模型，在真实医学影像任务上表现具有竞争力。
摘要：
基础模型为人工智能在医疗保健领域的应用开辟了新的可能性。然而，即使在健康数据上进行了预训练，它们仍然需要针对特定的下游任务进行微调。此外，尽管基础模型减少了实现良好性能所需的训练数据量，但获取足够的数据仍然是一个挑战。这在一定程度上是由于为了保护患者隐私而对来自不同来源的数据的共享和聚合施加了限制。一种可能的解决方案是通过跨多个参与客户端（即医院、诊所等）的联邦学习来微调基础模型。在这项工作中，我们提出了一种新的个性化联邦微调方法，该方法学习正交LoRA适配器以解耦通用知识和客户端特定知识，使每个客户端能够充分利用自身数据和其他客户端的数据。我们在真实世界联邦医学影像任务上的初步结果表明，我们的方法与当前的联邦微调方法相比具有竞争力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
