Clustering in graphs has been a well-known research problem, particularly because most Internet and social network data is in the form of graphs. Organizations widely use spectral clustering algorithms to find clustering in graph datasets. However, applying spectral clustering to a large dataset is challenging due to computational overhead. While the distributed spectral clustering algorithm exists, they face the problem of data privacy and increased communication costs between the clients. Thus, in this paper, we propose a spectral clustering algorithm using federated learning (FL) to overcome these issues. FL is a privacy-protecting algorithm that accumulates model parameters from each local learner rather than collecting users' raw data, thus providing both scalability and data privacy. We developed two approaches: FedSpectral and FedSpectral+. FedSpectral is a baseline approach that uses local spectral clustering labels to aggregate the global spectral clustering by creating a similarity graph. FedSpectral+, a state-of-the-art approach, uses the power iteration method to learn the global spectral embedding by incorporating the entire graph data without access to the raw information distributed among the clients. We further designed our own similarity metric to check the clustering quality of the distributed approach to that of the original/non-FL clustering. The proposed approach FedSpectral+ obtained a similarity of 98.85% and 99.8%, comparable to that of global clustering on the ego-Facebook and email-Eu-core dataset.
A new type of federated clustering: A non-model-sharing approach
Center for Artificial Intelligence Research（人工智能研究中心）；Tsukuba Institute for Advanced Research（茨口高级研究机构）；University of Tsukuba（茨口大学）；Graduate School of Science and Technology（科学与技术研究生院）；Institute of Systems and Information Engineering（系统与信息工程研究所）
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering
Beijing Key Laboratory of Intelligent Communication Software and Multimedia（北京智能通信软件与多媒体重点实验室）；School of Computer Science (National Pilot Software Engineering School)（计算机学院（国家级试点软件学院））；Beijing University of Posts and Telecommunications（北京邮电大学）
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来智能网络重点实验室）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；School of Engineering, Westlake University（西湖大学工程学院）
Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory
Osaka Metropolitan University（大阪公立大学）；Okayama University（冈山大学）；Universiti Malaya（马来亚大学）；Southern University of Science and Technology（南方科技大学）；Tokyo Metropolitan University（东京都立大学）
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application
College of Computing and Informatics, Sungkyunkwan University（成均馆大学计算与信息学院）；Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute（电子通信研究院生物医学IT融合研究部）；Samsung Medical Center（三星医疗中心）
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；School of Software, Dalian University of Technology（大连理工大学软件学院）；School of Telecommunications Engineering, Xidian University（西安电子科技大学电信工程学院）
A Joint Gradient and Loss Based Clustered Federated Learning Design
University of Miami（迈阿密大学）；Frost Institute for Data Science and Computing, University of Miami（迈阿密大学弗罗斯特数据科学与计算研究所）；Zhejiang University（浙江大学）；North Carolina State University（北卡罗来纳州立大学）
Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳市人工智能与机器人研究院）；School of Engineering, Westlake University（西湖大学工学院）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）
One-Shot Federated Clustering of Non-Independent Completely Distributed Data
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Department of Computer Science, Beijing Normal-Hong Kong Baptist University（北京师范大学-香港 Baptist大学计算机科学系）；Department of Computer Science, Harbin Institute of Technology（哈尔滨工业大学计算机科学系）
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；University of Southern California（南加州大学）
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；LSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Noah’s Ark Lab, Huawei（华为诺亚实验室）
A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（计算机科学与人工智能学院，武汉科技大学）；Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology（交通物联网湖北省重点实验室，武汉科技大学）；School of Engineering, Swinburne University of Technology（工程学院，斯威本科技大学）；School of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）；State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Tongji Hospital, Tongji University（同济大学附属同济医院）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61研究院）；Guangzhou Huatu Information Technology Co., Ltd.（广州华图信息技术有限公司）；School of Electrical and Computer Engineering, The University of Sydney（悉尼大学电气与计算机工程学院）
