FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
This work addresses the problem of efficient and personalized federated learning for healthcare applications, offering incremental improvements by introducing a new benchmark and framework.
The authors tackled the challenge of applying federated learning to multimodal healthcare data with limited computational resources by developing FHBench, a benchmark from real-world datasets, and EPFL, a personalized framework, which demonstrated superior efficiency and effectiveness across healthcare modalities.
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.