Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Existing approaches often assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. Despite their success, these methods also present limitations when dealing with practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of both single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel FedMVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods. The code is available at \url{https://github.com/5Martina5/FMCSC}.
Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models
School of Computing Technologies（计算技术学院）；RMIT University（皇家墨尔本理工大学）；Department of Computing Technologies（计算技术系）；Swinburne University of Technology（斯威本理工大学）
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）
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
Department of ECE and INMC, Seoul National University, Seoul, South Korea（首尔国立大学电子与信息科学系及网络信息中心，首尔，韩国）；Graduate School of Data Science, Seoul National University, Seoul, South Korea（首尔国立大学数据科学研究生院，首尔，韩国）；Department of Computer Science, University of Colorado Boulder, CO, USA（科罗拉多大学博尔德分校计算机科学系，科罗拉多州，美国）
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究院，东方理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Jiao Tong University（上海交通大学）；Research Institute for Smart Energy, The Hong Kong Polytechnic University（香港理工大学智慧能源研究院）
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；School of Public Affairs, Zhejiang University, China（浙江大学公共管理学院）
Trustless Federated Learning at Edge-Scale: A Compositional Architecture for Decentralized, Verifiable, and Incentive-Aligned Coordination
Marshall School of Business University of Southern California（南加州大学马歇尔商学院）；School of Natural and Computing Sciences University of Aberdeen（阿伯丁大学自然与计算科学学院）
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University（杜克大学）；Singapore General Hospital（新加坡中央医院）；Columbia University Mailman School of Public Health（哥伦比亚大学梅尔曼公共卫生学院）；National University of Singapore（新加坡国立大学）
PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs
School of Artificial Intelligence and Big Data, Hefei University（合肥大学人工智能与大数据学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）
Bridging Through Absence: How Comeback Researchers Bridge Knowledge Gaps Through Structural Re-emergence
University College Cork（都柏林大学学院）；Shanghai Jiao Tong University（上海交通大学）；Indian Institute of Information Technology（印度信息科技大学）；Techno India University（Techno India大学）
Adaptive Dual-Teacher Distillation with Subnetwork Rectification for Bridging Semantic Gaps in Black-Box Domain Adaptation
School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）；School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）
Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning
Massachusetts Institute of Technology（麻省理工学院）；Havard Univeristy（哈佛大学）；LG CNS；Université de Montréal（蒙特利尔大学）；Mila（米拉研究所）；Google（谷歌）；Stanford University（斯坦福大学）
Bridging Performance Gaps for ECG Foundation Models: A Post-Training Strategy
Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（信息中心部门，阜外医院，中国医学科学院和北京协和医学院）；Function Test Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（功能测试中心，阜外医院，国家心血管疾病中心，中国医学科学院和北京协和医学院）；Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（心律失常中心，阜外医院，国家心血管疾病中心，中国医学科学院和北京协和医学院）；Center for Health Statistics and Information, National Health Commission People’s Republic of China（健康统计与信息中心，中华人民共和国国家卫生健康委员会）
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
National University of Singapore（新加坡国立大学）；WeBank（微众银行）；Zhejiang University（浙江大学）；Shanghai Jiao Tong University（上海交通大学）；Huazhong University of Science and Technology（华中科技大学）
Accurate Forgetting for Heterogeneous Federated Continual Learning
Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；The University of Auckland（奥克兰大学）；RIKEN（理化学研究所）；Hong Kong Baptist University（香港浸会大学）；DataCanvas Technology Co., Ltd.（数帆科技有限公司）；The University of Tokyo（东京大学）
