FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare

• URL: http://arxiv.org/abs/2504.10817v1
• Date: Tue, 15 Apr 2025 02:38:00 GMT
• Title: FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
• Authors: Penghao Wang, Qian Chen, Teng Zhang, Yingwei Zhang, Wang Lu, Yiqiang Chen,
• Abstract summary: Federated Learning (FL) has emerged as an effective solution for multi-institutional collaborations without sharing patient data.<n>We developed the Federated Healthcare Benchmark(FHBench), a benchmark specifically designed from datasets derived from real-world healthcare applications.<n>Building on FHBench, we introduced Efficient Personalized Federated Learning with Adaptive LoRA(EPFL), a personalized FL framework.
• Score: 19.559006138457605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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