Med-MMFL：医疗领域的多模态联邦学习基准
Med-MMFL: A Multimodal Federated Learning Benchmark in Healthcare
AI总结：
Med-MMFL是首个医疗领域多模态联邦学习基准，涵盖多种模态和任务，评估六种先进算法以推动医疗多模态联邦学习的研究。
摘要：
联邦学习（FL）能够在去中心化的医疗机构之间进行协作模型训练，同时保护数据隐私。然而，医疗领域的FL基准仍然稀缺，现有的研究主要集中在单模态或双模态以及有限的医疗任务上。这一空白凸显了需要标准化评估以推动系统性理解在医疗多模态FL（MMFL）中的重要性。为此，我们介绍了Med-MMFL，首个针对医疗领域的综合性MMFL基准，涵盖多种模态、任务和联邦场景。我们的基准评估了六个代表性的最新FL算法，涵盖不同的聚合策略、损失公式和正则化技术。它覆盖了具有2到4种模态的数据集，共包含10种独特的医疗模态，包括文本、病理图像、心电图、X光、放射科报告和多种MRI序列。实验在自然联邦、合成IID和合成非IID设置下进行，以模拟现实世界中的异质性。我们评估了分割、分类、模态对齐（检索）和VQA任务。为了支持可重复性和在现实医疗场景下公平比较未来的多模态联邦学习（MMFL）方法，我们发布了完整的基准实现，包括数据处理和分区管道，网址为https://github.com/bhattarailab/Med-MMFL-Benchmark。
英文摘要：
Federated learning (FL) enables collaborative model training across decentralized medical institutions while preserving data privacy. However, medical FL benchmarks remain scarce, with existing efforts focusing mainly on unimodal or bimodal modalities and a limited range of medical tasks. This gap underscores the need for standardized evaluation to advance systematic understanding in medical MultiModal FL (MMFL). To this end, we introduce Med-MMFL, the first comprehensive MMFL benchmark for the medical domain, encompassing diverse modalities, tasks, and federation scenarios. Our benchmark evaluates six representative state-of-the-art FL algorithms, covering different aggregation strategies, loss formulations, and regularization techniques. It spans datasets with 2 to 4 modalities, comprising a total of 10 unique medical modalities, including text, pathology images, ECG, X-ray, radiology reports, and multiple MRI sequences. Experiments are conducted across naturally federated, synthetic IID, and synthetic non-IID settings to simulate real-world heterogeneity. We assess segmentation, classification, modality alignment (retrieval), and VQA tasks. To support reproducibility and fair comparison of future multimodal federated learning (MMFL) methods under realistic medical settings, we release the complete benchmark implementation, including data processing and partitioning pipelines, at https://github.com/bhattarailab/Med-MMFL-Benchmark .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
