Large language models (LLMs) show great promise in healthcare, but their applications are hindered by data privacy restrictions and the challenges of cross-institution collaboration. Sensitive medical data cannot be centralized, while non-independent and identically distributed (non-IID) characteristics across institutions further complicate convergence and fairness. To address these issues, we present a federated fine-tuning approach based on Low-Rank Adaptation (LoRA), enabling privacy-preserving knowledge flow across institutions. The method iteratively combines local LoRA adaptation with global parameter aggregation, allowing efficient knowledge sharing without exposing raw data. A blockchain identity scheme is used for identifying individual LLM in such a distributed network. We evaluate this approach on heterogeneous and highly non-IID medical text datasets, where experiments demonstrate that federated LoRA not only enhances cross-client generalization but also improves the performance of the weakest client, achieving stable convergence and fairer outcomes. These findings highlight federated LoRA fine-tuning as a practical and effective paradigm for adapting LLMs in healthcare, offering a new path for multi-center medical AI collaboration.

翻译：大语言模型（LLMs）在医疗健康领域展现出巨大潜力，但其应用受到数据隐私限制和跨机构协作挑战的阻碍。敏感医疗数据无法集中处理，而机构间非独立同分布（non-IID）的特性进一步加剧了模型收敛与公平性的复杂性。为解决这些问题，我们提出一种基于低秩自适应（LoRA）的联邦微调方法，实现跨机构的隐私保护知识流动。该方法通过局部LoRA适应与全局参数聚合的迭代结合，在无需暴露原始数据的前提下实现高效知识共享。我们采用区块链身份方案对该分布式网络中的个体LLM进行标识。我们在异构且高度非独立同分布的医疗文本数据集上评估该方法，实验表明联邦LoRA不仅增强了跨客户端的泛化能力，还提升了最弱客户端的性能，实现了稳定收敛与更公平的结果。这些发现凸显了联邦LoRA微调作为医疗领域LLM适配的实用有效范式，为多中心医疗AI协作开辟了新路径。