Federated Learning (FL) has emerged as an effective solution for multi-institutional collaborations without sharing patient data, offering a range of methods tailored for diverse applications. However, real-world medical datasets are often multimodal, and computational resources are limited, posing significant challenges for existing FL approaches. Recognizing these limitations, we developed the Federated Healthcare Benchmark(FHBench), a benchmark specifically designed from datasets derived from real-world healthcare applications. FHBench encompasses critical diagnostic tasks across domains such as the nervous, cardiovascular, and respiratory systems and general pathology, providing comprehensive support for multimodal healthcare evaluations and filling a significant gap in existing benchmarks. Building on FHBench, we introduced Efficient Personalized Federated Learning with Adaptive LoRA(EPFL), a personalized FL framework that demonstrates superior efficiency and effectiveness across various healthcare modalities. Our results highlight the robustness of FHBench as a benchmarking tool and the potential of EPFL as an innovative approach to advancing healthcare-focused FL, addressing key limitations of existing methods.

该研究解决了当前联邦学习在多模态医疗数据处理中的效率与个性化不足的问题。通过开发联邦医疗基准FHBench及其上的高效个性化联邦学习框架EPFL，研究展示了一种新的方法，有效提升了医疗数据处理的效率和效果，特别是在神经、心血管和呼吸等多个领域。研究结果表明，该工具和方法将显著推动医疗领域的联邦学习进展。

FHBench：面向高效个性化的联邦学习用于多模态医疗