Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering
Elmore Family School of Electrical and Computer Engineering, Purdue University（电气与计算机工程学院，普渡大学）；Department of Electrical Engineering, University at Buffalo–SUNY（电气工程系，布法罗大学–SUNY）
Shahryar Zehtabi, Seyyedali Hosseinalipour, Christopher G. Brinton
Federated learning (FL) is a popular technique for distributing machine learning (ML) across a set of edge devices. In this paper, we study fully decentralized FL, where in addition to devices conducting training locally, they carry out model aggregations via cooperative consensus formation over device-to-device (D2D) networks. We introduce asynchronous, event-triggered communications among the devices to handle settings where access to a central server is not feasible. To account for the inherent resource heterogeneity and statistical diversity challenges in FL, we define personalized communication triggering conditions at each device that weigh the change in local model parameters against the available local network resources. We theoretically recover the $O(\ln{k} / \sqrt{k})$ convergence rate to the globally optimal model of decentralized gradient descent (DGD) methods in the setup of our methodology. We provide our convergence guarantees for the last iterates of models, under relaxed graph connectivity and data heterogeneity assumptions compared with the existing literature. To do so, we demonstrate a $B$-connected information flow guarantee in the presence of sporadic communications over the time-varying D2D graph. Our subsequent numerical evaluations demonstrate that our methodology obtains substantial improvements in convergence speed and/or communication savings compared to existing decentralized FL baselines.
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Nanjing University of Science and Technology（南京理工大学）；Tsinghua University（清华大学）；National University of Defense Technology（国防科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；JD Explore Academy（京东探索研究院）
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Tsinghua University（清华大学）；Nanjing University of Science and Technology（南京理工大学）；The University of Sydney（悉尼大学）；Virginia Tech（弗吉尼亚理工大学）；JD Explore Academy（京东探索研究院）
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Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Ivannikov Institute for System Programming RAS（俄罗斯科学院伊万尼科夫系统编程研究所）；Bosch Center for Artificial Intelligence（博世人工智能中心）
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Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；School of Engineering and Technology, Central Queensland University（中央昆士兰大学工程与技术学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
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University of Science and Technology of China（中国科学技术大学）；JD Explore Academy（京东探索研究院）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Institute of Artificial Intelligence（人工智能研究院）
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School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；School of Cyber Science and Technology, Shandong University（山东大学网络空间安全学院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）；Department of Computer Science, University of Illinois at Chicago（伊利诺伊大学芝加哥分校计算机科学系）
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School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；Jiangsu Key University Laboratory of Software and Media Technology under Human-Computer Cooperation (Jiangnan University)（江南大学江苏省人机协同软件与媒体技术重点实验室）
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Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab（量子云计算与分布式系统实验室）；School of Computing and Information Systems（计算与信息系统学院）；The University of Melbourne, Australia（墨尔本大学）
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Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences)（计算能力网络与信息安全重点实验室，教育部，山东计算机科学中心，齐鲁大学（山东科学院））；Shandong Provincial Key Laboratory of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science（山东省工业网络与信息系统安全重点实验室，山东省计算机科学基础研究中心）；School of Information and Software Engineering, University of Electronic Science and Technology of China（信息与软件工程学院，电子科技大学）
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
City University of Hong Kong（香港城市大学）；Department of Electrical Engineering, City University of Hong Kong（香港城市大学电气工程系（注：根据提取规则，机构粒度到大学/学院/公司/研究院级别即可，此处仅提取到大学级别，故正确应为City University of Hong Kong））
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hamad Bin Khalifa University（哈马德·本·卡西姆大学）；Central South University（中南大学）
Personalized Federated Learning via Amortized Bayesian Meta-Learning
University of Electronic Science and Technology of China（电子科技大学）；Nanjing Audit University（南京审计大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng National Lab（鹏城国家实验室）
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Graduate School of AI, POSTECH, Pohang, Republic of Korea（POSTECH人工智能研究生院）；Department of CSE, POSTECH, Pohang, Republic of Korea（POSTECH计算机科学与工程系）；National AI Research Lab, Seoul, Republic of Korea（首尔国家人工智能研究实验室）
