Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University（杜克大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究院）；Singapore Health Services（新加坡保健服务集团）；Singapore General Hospital（新加坡中央医院）；Imperial College London（帝国理工学院）；National University of Singapore（新加坡国立大学）
Siqi Li, Di Miao, Qiming Wu, Chuan Hong, Danny D'Agostino, Xin Li, Yilin Ning, Yuqing Shang, Huazhu Fu, Marcus Eng Hock Ong, Hamed Haddadi, Nan Liu
联邦学习（FL）在医疗协作场景的数据隐私保护方面展现出可观潜力。尽管“FL”这一术语最初由工程界提出，统计学领域也已探索了类似的隐私保护算法，但统计类FL算法的认可度仍远低于工程类FL算法。本研究旨在填补这一认知缺口，首次对工程与统计两大领域的FL框架开展全面对比。我们使用模拟数据与真实世界数据，评估了5种FL框架。结果显示，统计类FL算法生成的模型系数点估计偏差更小，且能便捷地进行置信区间估计；相比之下，基于工程的方法往往能产出更准确的预测结果，部分情况下表现优于集中 pooled 模型与统计类FL模型。本研究明确了两类方法的相对优缺点，强调未来FL应用中需提升对两类方法的认知并推动二者融合。
英文摘要：
Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering community, the statistical field has also explored similar privacy-preserving algorithms. Statistical FL algorithms, however, remain considerably less recognized than their engineering counterparts. Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains. We evaluated five FL frameworks using both simulated and real-world data. The results indicate that statistical FL algorithms yield less biased point estimates for model coefficients and offer convenient confidence interval estimations. In contrast, engineering-based methods tend to generate more accurate predictions, sometimes surpassing central pooled and statistical FL models. This study underscores the relative strengths and weaknesses of both types of methods, emphasizing the need for increased awareness and their integration in future FL applications.
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Imperial College London（伦敦帝国学院）；Massachusetts General Hospital（麻省总医院）；Zhejiang University（浙江大学）；Weill Cornell Medicine of Cornell University（康奈尔大学威尔医学院）；Royal Brompton Hospital（皇家布朗普顿医院）；King’s College London（伦敦国王学院）
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；National Dental Research Institute Singapore（新加坡国家牙科研究所）；National Dental Centre Singapore（新加坡国家牙科中心）；Oral Health Academic Clinical Programme, Duke-NUS Medical School（杜克-新加坡国立大学医学院口腔健康学术临床项目）；Programme in Health Services and Systems Research, Duke-NUS Medical School（杜克-新加坡国立大学医学院卫生服务与系统研究项目）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore Eye Research Institute（新加坡眼科研究所）；Imperial College London（伦敦帝国理工学院）；Singapore General Hospital（新加坡中央医院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）
Department of Engineering Sciences and Applied Mathematics, Northwestern University（工程科学与应用数学系，西北大学）；Department of Industrial Engineering and Management Sciences, Northwestern University（工业工程与管理科学系，西北大学）
Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting
Utrecht University（乌得勒支大学）；KPMG N.V.（毕马威公司）；Eindhoven University of Technology（埃因霍温理工大学）；University Medical Centre Utrecht（乌得勒支大学医学中心）；Leiden University Medical Centre（莱顿大学医学中心）；Boğaziçi University（海峡大学）；Leiden University（莱顿大学）
Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness
ARTORG Center for Biomedical Engineering Research University of Bern（伯尔尼大学生物医学工程研究中心）；Graduate School for Cellular and Biomedical Sciences University of Bern（伯尔尼大学细胞与生物医学科学研究生院）
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）
Secure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP
School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology, Thammasat University（泰国朱拉安吞国际技术学院，泰国 Thammasat 大学）
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources
SVKM's Dwarkadas J. Sanghvi College of Engineering（SVKM's 德瓦卡达斯·J.桑格维工程学院）；Davidson School of Chemical Engineering, Purdue University（普渡大学戴维森化学工程学院）；Federal University of Rio de Janeiro（里约热内卢联邦大学）
Distribution-Free Federated Learning with Conformal Predictions
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院阿蒂努拉·A·马蒂诺斯生物医学成像中心）；Department of Radiology, Harvard Medical School（哈佛医学院放射学系）
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
FedMood: Federated Learning on Mobile Health Data for Mood Detection
School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间科学与技术学院）；Beijing Advanced Innovation Center for Big Data and Brain Computing（北京大数据与脑计算高精尖创新中心）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Fordham University（福特汉姆大学）；Lehigh University（理海大学）
Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology, Thammasat University（泰国朱拉隆功大学国际技术学院）
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）
LF2L: Loss Fusion Horizontal Federated Learning Across Heterogeneous Feature Spaces Using External Datasets Effectively: A Case Study in Second Primary Cancer Prediction
National Yang Ming Chiao Tung University（国立阳明交通大学）
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
Seoul National University Bundang Hospital（首尔大学盆唐医院）；Maastricht University（马斯特里赫特大学）；Pfizer Pharma GmbH（辉瑞制药有限公司）；Queen Mary University of London（伦敦玛丽女王大学）；University of Oxford（牛津大学）；Imperial College London（帝国理工学院）；Flower Labs；Academy of Athens（雅典科学院）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿罗森多夫亥姆霍兹中心）；Pfizer Inc（辉瑞公司）
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University（杜克大学）；Kyoto University（京都大学）；Singapore Health Services（新加坡保健服务）；Singapore General Hospital（新加坡中央医院）；NUS Artificial Intelligence Institute（新加坡国立大学人工智能研究所）；National University of Singapore（新加坡国立大学）
