FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
Journal:
arXiv
Published Date:
Apr 15, 2025
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.
