Federated learning provides a framework to address the challenges of distributed computing, data ownership and privacy over a large number of distributed clients with low computational and communication capabilities. In this paper, we study the problem of learning the exact support of sparse linear regression in the federated learning setup. We provide a simple communication efficient algorithm which only needs one-shot communication with the centralized server to compute the exact support. Our method does not require the clients to solve any optimization problem and thus, can be run on devices with low computational capabilities. Our method is naturally robust to the problems of client failure, model poisoning and straggling clients. We formally prove that our method requires a number of samples per client that is polynomial with respect to the support size, but independent of the dimension of the problem. We require the number of distributed clients to be logarithmic in the dimension of the problem. If the predictor variables are mutually independent then the overall sample complexity matches the optimal sample complexity of the non-federated centralized setting. Furthermore, our method is easy to implement and has an overall polynomial time complexity.
Distribution Alignment for One-Shot Federated Learning via Optimal Transport
AI for Good (AIGO), Italian Institute of Technology（意大利技术研究院AI for Good (AIGO)）；MaLGa-DIBRIS, University of Genoa（热那亚大学MaLGa-DIBRIS）；Department of Computer Science, University of Verona（维罗纳大学计算机科学系）
Federated Unlearning Model Recovery in Data with Skewed Label Distributions
School of Computer Science and Technology（计算机科学与技术学院）；Dalian University of Technology（大连理工大学）；School of Engineering and Computer Science（工程与计算机科学学院）；Victoria University of Wellington（惠灵顿维多利亚大学）
FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
School of Cyber Engineering, Xidian University（电子科技大学信息工程学院）；RMIT University（皇家墨尔本理工大学）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（澳大利亚联邦科学与工业研究组织）；UNSW Sydney（新南威尔士大学悉尼分校）；University of California, Davis（加州大学戴维斯分校）
Task Arithmetic Through The Lens Of One-Shot Federated Learning
Princeton University（普林斯顿大学）；Fujitsu Research of America（富士通美国研究部）；Operations Research and Financial Engineering（运筹学与金融工程）；Electrical and Computer Engineering（电气与计算机工程）
LightTR: A Lightweight Framework for Federated Trajectory Recovery
University of Electronic Science and Technology of China（电子科技大学）；Aalborg University（奥尔堡大学）；Nanyang Technological University（南洋理工大学）；Zhejiang University（浙江大学）
When to Stop Federated Learning: Zero-Shot Generation of Synthetic Validation Data with Generative AI for Early Stopping
Institute of Information & Communications Technology Planning & Evaluation (IITP)-ITRC (Information Technology Research Center)（信息与通信技术规划与评估机构（IITP）-ITRC（信息技术研究中心））；Institute of Science Tokyo（东京科学研究所）
Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；University of Michigan（密歇根大学）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation
Hong Kong Baptist University（香港浸会大学）；Narayana Superspeciality Hospital（纳拉亚纳超级专科医院）；National Institute of Technology Durgapur（杜尔加布尔国家理工学院）；Indian Institute of Technology（印度理工学院）
Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preserving
College of Computer and Information Science（计算机与信息科学学院）；Southwest University（西南大学）；School of Engineering（工程学院）；University of Western Australia（西澳大学）；College of Food Science（食品科学学院）；Chongqing True Flavor Food Co LTD（重庆真味食品有限公司）
Capture Global Feature Statistics for One-Shot Federated Learning
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；State Key Laboratory of Cyberspace Security Defense（网络空间安全防御国家重点实验室）
FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
National University of Singapore（新加坡国立大学）；Northeastern University（东北大学）；University of Technology Sydney（悉尼科技大学）；Tsinghua University（清华大学）；ETH Zurich（苏黎世联邦理工学院）
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
Department of Electrical Engineering and Information Technology, University of Naples Federico II（那不勒斯费德里科二世大学电气工程与信息科技系）；Department of Mathematics and Applications "R. Caccioppoli", University of Naples Federico II（那不勒斯费德里科二世大学数学与应用系）；Department of Mathematics, Indian Institute of Technology Indore（印度理工学院Indore分校数学系）
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
Seoul National University Hospital（首尔国立大学医院）；Chung-Ang University（中央大学）；Korea University（高丽大学）；Deakin University（迪肯大学）；University of Oulu（奥卢大学）；Yonsei University（延世大学）
