Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy. Recently, there have been rising concerns on the distributional discrepancies across different clients, which could even cause counterproductive consequences when collaborating with others. While it is not necessarily that collaborating with all clients will achieve the best performance, in this paper, we study a rational collaboration called ``collaboration equilibrium'' (CE), where smaller collaboration coalitions are formed. Each client collaborates with certain members who maximally improve the model learning and isolates the others who make little contribution. We propose the concept of benefit graph which describes how each client can benefit from collaborating with other clients and advance a Pareto optimization approach to identify the optimal collaborators. Then we theoretically prove that we can reach a CE from the benefit graph through an iterative graph operation. Our framework provides a new way of setting up collaborations in a research network. Experiments on both synthetic and real world data sets are provided to demonstrate the effectiveness of our method.
Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data
Graduate School of Science and Technology, University of Tsukuba（茨川大学理学技术研究生院）；Center for Artificial Intelligence Research, Tsukuba Institute for Advanced Research, University of Tsukuba（茨川大学先进研究所人工智能研究中心）；Institute of Systems and Information Engineering, University of Tsukuba（茨川大学系统与信息工程研究所）
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
Cornell University（康奈尔大学）；University of Minnesota（明尼苏达大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Illinois Institute of Technology（伊利诺伊理工大学）
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
School of Computing and Information Technology, University of Wollongong, Wollongong, Australia（1 计算与信息科技学院，沃林根大学，澳大利亚）；School of Electrical and Computer Engineering, The University of Sydney, Sydney, Australia（2 电气与计算机工程学院，悉尼大学，澳大利亚）；School of Cyber Engineering, Xidian University, Xi’an, China（3 网络工程学院，西安电子科技大学，中国）；QLD Alliance for Agriculture and Food, The University of Queensland, Brisbane, Australia（4 女王艾丽丝州农业与食品联盟，昆士兰大学，澳大利亚）；Principal Research Scientist, CSIRO Data61, Eveleigh, Australia（5 主要研究员，CSIRO Data61，澳大利亚）
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
ENSIAS, Mohammed V University in Rabat（拉巴特穆罕默德五世大学ENSIAS学院）；Higher School of Technology, Hassan II University in Casablanca（卡萨布兰卡哈桑二世大学高等技术学院）；School of Science and Engineering, Al Akhawayn University in Ifrane（伊夫兰阿卡韦恩大学科学与工程学院）
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Laboratory（中关村实验室）；Beijing Jiaotong University（北京交通大学）
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification
University Hospital Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN（埃尔朗根-EMN综合癌症中心）
Universal Adversarial Backdoor Attacks to Fool Vertical Federated Learning in Cloud-Edge Collaboration
Fudan University（复旦大学）；Engineering Research Center of Cyber Security Auditing and Monitoring, Ministry of Education（教育部网络安全审计与监控工程研究中心）；Shanghai Blockchain Engineering Research Center（上海区块链工程研究中心）；Institute of Financial Technology, Fudan University（复旦大学金融科技研究院）
针对云边协同 VFL 二分类场景，提出无需额外数据的 UAB 干净标签后门攻击，通过交替优化通用触发器与子问题参数注入后门，在两个数据集上达到最高 100% 攻击性能且不损害主任务。
A Novel Collaborative Framework for Efficient Synchronization in Split Federated Learning over Wireless Networks
Department of Electrical and Computer Engineering, Concordia University（电气与计算机工程系，康科迪亚大学）；Department of Physics and Computer Science, Faculty of Science, Wilfrid Laurier University（物理与计算机科学系，科学学院，威尔弗里德·劳里埃大学）
FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworks
Tsinghua University（清华大学）；Fuyao University of Science and Technology（福耀科技大学）；Hong Kong University of Science and Technology（香港科技大学）；Shenzhen University（深圳大学）；Southern University of Science and Technology（南方科技大学）
Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
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）
Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration
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）
From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland Institute for Health Computing（马里兰大学健康计算研究所）；St. Jude Children’s Research Hospital（圣犹大儿童研究医院）
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources
SVKM's Dwarkadas J. Sanghvi College of Engineering（SVKM's 德瓦卡达斯·J.桑格维工程学院）；Davidson School of Chemical Engineering, Purdue University（普渡大学戴维森化学工程学院）；Federal University of Rio de Janeiro（里约热内卢联邦大学）
Decentralized Directed Collaboration for Personalized Federated Learning
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）
Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border Collaboration
IHU Strasbourg（斯特拉斯堡大学医院研究所）；University of Strasbourg（斯特拉斯堡大学）；University Hospital Basel（巴塞尔大学医院）；Medical Center – University of Freiburg（弗莱堡大学医学中心）；Faculty of Medicine, University of Freiburg（弗莱堡大学医学院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Bosch Digital Innovation Hub I Koordinierungsstelle Telemedizin Baden-Württemberg (BDIH I KTBW)（博世数字创新中心 I 巴登-符腾堡远程医疗协调办公室）；Bosch Health Campus (BHC)（博世健康园区）
Clinnova 计划通过法德瑞卢跨境联邦学习概念验证，在 MRI 图像多发性硬化症分割上验证了数据联邦化与 AI 协作的可行性，并总结了技术、后勤与伦理方面的关键经验。
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Beijing University of Posts and Telecommunications（北京邮电大学）；Nanjing University（南京大学）；Nanyang Technological University（南洋理工大学）；Agency for Science, Technology and Research（新加坡科技研究局）
Wind turbine condition monitoring based on intra- and inter-farm federated learning
Bern University of Applied Sciences, School of Engineering（伯恩应用科学大学工程学院）；Università della Svizzera italiana, Faculty of Informatics（瑞士联合大学信息学院）；Delft University of Technology, Department of Geoscience（代尔夫特理工大学地质学院）；Remote Sensing, Stevinweg 1, 2628 Delft, The Netherlands（遥感学院，Stevinweg 1，2628 Delft，荷兰）
Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning
Beijing University of Posts and Telecommunications（北京邮电大学）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Centre for Frontier AI Research, Agency for Science, Technology and Research（新加坡科技研究局前沿人工智能研究中心）；School of Engineering, Westlake University（西湖大学工程学院）
Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation
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））
