Federated learning (FL) is an active area of research. One of the most suitable areas for adopting FL is the medical domain, where patient privacy must be respected. Previous research, however, does not provide a practical guide to applying FL in the medical domain. We propose empirical benchmarks and experimental settings for three representative medical datasets with different modalities: longitudinal electronic health records, skin cancer images, and electrocardiogram signals. The likely users of FL such as medical institutions and IT companies can take these benchmarks as guides for adopting FL and minimize their trial and error. For each dataset, each client data is from a different source to preserve real-world heterogeneity. We evaluate six FL algorithms designed for addressing data heterogeneity among clients, and a hybrid algorithm combining the strengths of two representative FL algorithms. Based on experiment results from three modalities, we discover that simple FL algorithms tend to outperform more sophisticated ones, while the hybrid algorithm consistently shows good, if not the best performance. We also find that a frequent global model update leads to better performance under a fixed training iteration budget. As the number of participating clients increases, higher cost is incurred due to increased IT administrators and GPUs, but the performance consistently increases. We expect future users will refer to these empirical benchmarks to design the FL experiments in the medical domain considering their clinical tasks and obtain stronger performance with lower costs.
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；State University of New York at Buffalo（纽约州立大学布法罗分校）
Towards Case-based Interpretability for Medical Federated Learning
The Netherlands Cancer Institute（荷兰癌症研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；Utrecht University（乌得勒支大学）；INESC TEC
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；State University of New York at Buffalo（纽约州立大学布法罗分校）
Federated Learning with Differential Privacy: An Utility-Enhanced Approach
Deakin University（迪肯大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；University of New South Wales (UNSW)（新南威尔士大学）
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Engineering, Hong Kong University of Science and Technology（香港科技大学工程学院）
No Free Lunch Theorem for Security and Utility in Federated Learning
Hong Kong University of Science and Technology（香港科技大学）；Webank（微众银行）
在联邦学习场景中，多方从各自的数据中联合学习模型，对于选择合适的算法存在两个相互冲突的目标。一方面，在\textit{semi-honest}合作伙伴在场的情况下，私人和敏感的训练数据必须尽可能地保持安全，而另一方面，为了学习效用，必须在不同方之间交换一定量的信息。这样的挑战需要隐私保护的联邦学习解决方案，它最大限度地提高了学习模型的效用，并保持了参与方的私人数据的可证明的隐私保证。本文阐述了一个一般框架，a）从统一的信息论角度阐述了隐私损失和效用损失之间的权衡，b）描绘了使用不同保护机制（包括随机化、稀疏性和同态加密）时隐私-效用权衡的定量界限。结果表明，在一般情况下，\textit{there is no free lunch for the privacy-utility trade-off}和一个交易的隐私保护与一定程度的降级效用。本文的定量分析对设计实用的联邦学习算法具有指导意义。
Cross-Domain Federated Learning in Medical Imaging
The Johns Hopkins University（约翰斯·霍普金斯大学）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Sidney Kimmel Comprehensive Center（西德尼·金梅尔综合中心）
Theoretically Principled Federated Learning for Balancing Privacy and Utility
Huazhong University of Science and Technology（华中科技大学）；Tsinghua University（清华大学）；Peng Cheng Laboratory（鹏城实验室）；WeBank（微众银行）；The Hong Kong Polytechnic University（香港理工大学）
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise
Academy for Engineering and Technology, Fudan University（复旦大学工程学院）；School of Computer Science, China University of Geosciences（中国地质大学（武汉）计算机学院）；College of Science, Beijing Forestry University（北京林业大学理学院）
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
The Chinese University of Hong Kong（香港中文大学）
联合学习（FL）允许多个医疗机构协作学习全球模型，而无需集中客户数据。由于来自各种扫描仪和患者人口统计学的医学图像的异质性，这种全局模型很难（如果可能的话）为每个单独的客户端共同实现最佳性能。当将全局模型部署到FL之外的看不见的客户端时，这个问题变得更加严重，其中在联邦训练期间没有呈现看不见的分布。为了优化医疗成像任务的每个单独的客户端的预测精度，我们提出了一种新的统一的框架\textit{Inside and Outside model Personalization in FL}（IOP-FL）。我们的内部个性化使用了一种轻量级的基于梯度的方法，该方法通过积累用于公共知识的全局梯度和用于特定于客户端的优化的局部梯度，为每个客户端利用本地适应模型。此外，并且重要的是，所获得的局部个性化模型和全局模型可以形成多样且信息丰富的路由空间，以针对外部FL客户端个性化适配模型。因此，我们设计了一个新的测试时路由方案，使用形状约束的一致性损失，动态地将模型，给定的分布信息所传达的测试数据。我们在两个医学图像分割任务上的广泛实验结果在内部和外部个性化方面都比SOTA方法有了显着的改进，证明了我们的IOP-FL方案在临床实践中的潜力。
MedFedPure: A Medical Federated Framework with MAE-based Detection and Diffusion Purification for Inference-Time Attacks
School of Electrical and Computer Engineering（电气与计算机工程学院）；Max Planck Institute for Brain Research（马克斯·普朗克脑研究所在法兰克福）；School of Computer Engineering,University of Science and Technology (IUST)（科学技术大学（IUST）计算机工程学院）；Department of Electrical and Computer Engineering（电气与计算机工程系）；University of Waterloo（滑铁卢大学）
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering
Graduate School of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术研究院）；Faculty of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术学院）
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case
University of Stavanger（斯塔万格大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Old Dominion University（奥尔多明尼昂大学）；Norwegian University of Science and Technology（挪威科技大学）
