With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in \ac{FL} have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable \ac{FL} models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of \ac{FL}, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.
Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning
Technical University of Denmark（丹麦技术大学）
联邦学习的思想是协同训练深度神经网络模型，并与多个参与者共享它们，而不会相互暴露他们的私人训练数据。由于患者的隐私记录，这在医疗领域非常有吸引力。然而，最近提出的一种名为Deep Leakage from Concentrants的方法使攻击者能够从共享梯度中重建数据。这项研究表明，它是多么容易重建不同的数据初始化方案和距离措施的图像。我们展示了数据和模型架构如何影响初始化方案和距离测量配置的最佳选择时，与单图像。我们证明了初始化方案和距离测量的选择可以显着提高收敛速度和质量。此外，我们发现，最佳的攻击配置在很大程度上取决于目标图像分布的性质和模型架构的复杂性。
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）
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）
Benchmark Evaluation of Federated Learning on Multi-organ Images
Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University（湖南生物信息省重点实验室，计算机科学与工程学院，中南大学）；Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of software, Xinjiang University（新疆大数据与智能软件工程研究中心，软件学院，新疆大学）
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）
Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans
University of Electronic Science and Technology of China（电子科技大学）；Macau University of Science and Technology（澳门科技大学）；Southwest Jiaotong University（西南交通大学）；China Telecommunications Corporation（中国电信集团有限公司）
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）
CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；RWTH Aachen University（亚琛工业大学）；Medizinische Hochschule Hannover（汉诺威医学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
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。
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
A New Perspective to Boost Performance Fairness for Medical Federated Learning
Australian National University（澳大利亚国立大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangxi Medical University（广西医科大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；The Hong Kong University of Science and Technology（香港科技大学）
