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
Laura Latorre, Liliana Petrychenko, Regina Beets-Tan, Taisiya Kopytova, Wilson Silva
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We explore deep generative models to generate case-based explanations in a medical federated learning setting. Explaining AI model decisions through case-based interpretability is paramount to increasing trust and allowing widespread adoption of AI in clinical practice. However, medical AI training paradigms are shifting towards federated learning settings in order to comply with data protection regulations. In a federated scenario, past data is inaccessible to the current user. Thus, we use a deep generative model to generate synthetic examples that protect privacy and explain decisions. Our proof-of-concept focuses on pleural effusion diagnosis and uses publicly available Chest X-ray data.
FedNAMs: Performing Interpretability Analysis in Federated Learning Context
Department of Electrical and Computer Engineering University of California San Diego（电气与计算机工程系加州大学圣地亚哥分校）；Department of Computer Science and Engineering University of California San Diego（计算机科学与工程系加州大学圣地亚哥分校）
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）
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）
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness
Mepco Schlenk Engineering College（梅普科施伦克工程学院）；Fordham University（福特汉姆大学）；University of South Florida（南佛罗里达大学）；Hung Yen University of Technology and Education（兴安工业大学）；Royal Military College of Canada（加拿大皇家军事学院）
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）
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）
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）
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）
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）
Department of Medical Data Science（医学数据科学系）；Wakayama Medical University（福冈医疗大学）；Department of Biomedical Sciences and Informatics（生物医学科学与信息学系）；Doshisha University（多摩大学）；Department of Biostatistics（生物统计学系）
FedBiCross: Personalized One-Shot Federated Learning on Medical Images
School of Computer Science and Engineering, Northwestern Polytechnical University, China（西北工业大学计算机科学与工程学院）；School of Science and Technology, Hong Kong Metropolitan University, Hong Kong（香港 Metropolitan 大学科学与技术学院）；Department of Computer Science, Hong Kong Baptist University, Hong Kong（香港 Baptist 大学计算机科学系）
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。
Dynamic Fusion based Federated Learning for COVID-19 Detection
China University of Petroleum (East China)（中国石油大学（华东））；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Southern University of Science and Technology（南方科技大学）
