构建医疗多模态联邦学习基准，提升个性化效率与效果
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
- 基于真实医疗数据构建多模态联邦学习评测基准
- 提出自适应LoRA的高效个性化框架，适配多种医疗模态
- 适合医疗联邦学习研究者及临床数据协作团队
联邦学习（FL）为跨机构医疗合作提供不共享患者数据的解决方案，但现实医疗数据多为多模态且计算资源有限，现有方法面临挑战。为此，我们构建了真实医疗应用数据驱动的联邦医疗基准（FHBench），涵盖神经系统、心血管系统、呼吸系统及普通病理等关键诊断任务，支持多模态医疗评估，填补现有基准空白。基于FHBench，我们提出高效个性化联邦学习框架EPFL，采用自适应LoRA技术，在多种医疗模态下均展现卓越效率与有效性。实验验证了FHBench作为评测工具的鲁棒性，以及EPFL在推动医疗导向联邦学习方面的潜力，解决了现有方法的关键局限。
原文摘要 · Abstract (English)
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.
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