Realistic synthetic tabular data generation encounters significant challenges in preserving privacy, especially when dealing with sensitive information in domains like finance and healthcare. In this paper, we introduce \textit{Federated Tabular Diffusion} (FedTabDiff) for generating high-fidelity mixed-type tabular data without centralized access to the original tabular datasets. Leveraging the strengths of \textit{Denoising Diffusion Probabilistic Models} (DDPMs), our approach addresses the inherent complexities in tabular data, such as mixed attribute types and implicit relationships. More critically, FedTabDiff realizes a decentralized learning scheme that permits multiple entities to collaboratively train a generative model while respecting data privacy and locality. We extend DDPMs into the federated setting for tabular data generation, which includes a synchronous update scheme and weighted averaging for effective model aggregation. Experimental evaluations on real-world financial and medical datasets attest to the framework's capability to produce synthetic data that maintains high fidelity, utility, privacy, and coverage.
Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis
Deutsche Bundesbank（德国联邦银行）；University of St.Gallen（圣加尔大学）；Swiss Federal Audit Office（瑞士联邦审计局）；International Computer Science Institute (ICSI)（国际计算机科学研究所）
Contrastive Federated Learning with Tabular Data Silos
The University of Queensland（昆士兰大学）；Monash University（莫纳什大学）；Indonesia Taxation Office（印度尼西亚税务局）；Queensland University of Technology（昆士兰科技大学）；SUNY Albany（纽约州立大学奥尔巴尼分校）；Dartmouth College（达特茅斯学院）；Hong Kong Polytechnic University（香港理工大学）；Renmin University of China（中国人民大学）
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
TU Delft（代尔夫特理工大学）；ABB Corporate Research Switzerland（瑞士ABB企业研究）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（巴黎-罗克琴堡研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒尔研究实验室）
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data
School of Computer Science, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；Department of New Networks, Peng Cheng Laboratory（鹏城实验室新型网络部）
SoK: Challenges in Tabular Membership Inference Attacks
Department of Computer Science, Faculty of Sciences of the University of Porto, Portugal（葡萄牙波尔图大学科学学院计算机科学系）；SnT, University of Luxembourg, Luxembourg（卢森堡大学SnT学院）；TekPrivacy, Portugal（葡萄牙TekPrivacy）
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）
Concordia: Self-Improving Synthetic Tables for Federated LLMs
University of Manchester（曼彻斯特大学）；National University of Singapore（新加坡国立大学）；New York University（纽约大学）；University of Minnesota（明尼苏达大学）；Halmstad University（哈姆斯塔德大学）；Harvard University（哈佛大学）；University of Florida（佛罗里达大学）
Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data
University of New South Wales（新南威尔士大学）；Ingham Institute for Applied Medical Research（英汉姆应用医学研究所）；Liverpool and Macarthur Cancer Therapy Centres（利物浦和麦卡瑟癌症治疗中心）；University of Sydney（悉尼大学）；St James's Hospital（圣詹姆斯医院）；University of Leeds（利兹大学）
Principled Federated Random Forests for Heterogeneous Data
Inria（法国国家信息与自动化研究所）；PreMeDICaL（预医学实验室）；Inserm（法国国家医学研究院）；University of Montpellier（蒙彼利埃大学）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎城市大学）；CNRS（国家科学研究中心）；LPSM（巴黎高等师范学院）
Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning
Ikerlan Technology Research Center Arrasate Spain；University of the Basque Country UPV/EHU Spain；HiTZ Basque Center for Language Technology - Ixa, University of the Basque Country UPV/EHU Spain；Flower Labs \& University of Cambridge Cambridge United Kingdom；Ikerlan Technology Research Center；University of the Basque Country UPV/EHU；HiTZ Basque Center for Language Technology - Ixa, University of the Basque Country UPV/EHU；Flower Labs \& University of Cambridge
FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
Department of Computer Science University of the Western Cape（计算机科学系乌得勒支大学）；Department of Computer Science Missouri University of Science and Technology（计算机科学系密苏里科技大学）
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Flower Labs；University of Cambridge（剑桥大学）；University of Auckland（奥克兰大学）；University of Melbourne（墨尔本大学）；Vector Institute；Fraunhofer IMS（弗劳恩霍夫应用固体物理与材料力学研究所）；NetCompany；Gachon University（嘉泉大学）；Owkin；Sony AI（索尼人工智能）
Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
College of Computing and Data Science (CCDS), Nanyang Technological University, Singapore and Digital Trust Centre (DTC), Singapore（南洋理工大学计算与数据科学学院（CCDS）及新加坡数字信任中心（DTC））；College of Computing and Data Science (CCDS), Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院（CCDS））；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
M$^2$FedAQI: Multimodal Federated Learning for Air Quality Prediction on Heterogeneous Edge Devices
Department of Computer Science and Engineering, SRM University-AP, India（印度SRM大学-AP计算机科学与工程系）；Department of Mathematics and Computing, IIT (ISM) Dhanbad, India（印度德汉巴德IIT（ISM）数学与计算系）；Department of Mathematics and the Department of Computer Science, University of Central Florida, USA（美国中央佛罗里达大学数学系和计算机科学系）
GShield: Mitigating Poisoning Attacks in Federated Learning
Department of Computer Science and Engineering, JAIN (Deemed-to-be University), Kochi, India（计算机科学与工程系，贾因大学（认定大学），印度科钦）；Department of Science and Technological Innovation, University of Eastern Piedmont, V.le Teresa Michel, 11, Alessandria, Italy（科学技术创新系，东皮埃蒙特大学，意大利艾利斯安迪亚）；Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy（电气、计算机与生物医学工程系，皮埃蒙特大学，意大利）；Department of Computer Applications, Cochin University of Science and Technology, India（计算机应用系，科钦科学与技术大学，印度）
