Federated Learning rests on the notion of training a global model distributedly on various devices. Under this setting, users' devices perform computations on their own data and then share the results with the cloud server to update the global model. A fundamental issue in such systems is to effectively incentivize user participation. The users suffer from privacy leakage of their local data during the federated model training process. Without well-designed incentives, self-interested users will be unwilling to participate in federated learning tasks and contribute their private data. To bridge this gap, in this paper, we adopt the game theory to design an effective incentive mechanism, which selects users that are most likely to provide reliable data and compensates for their costs of privacy leakage. We formulate our problem as a two-stage Stackelberg game and solve the game's equilibrium. Effectiveness of the proposed mechanism is demonstrated by extensive simulations.
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）
A Contract Theory based Incentive Mechanism for Federated Learning
Software College, Northeastern University（东北大学软件学院）；Singapore University of Technology Design（新加坡科技设计大学）；Nanyang Technological University（南洋理工大学）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)（南洋理工大学-不列颠哥伦比亚大学老年活跃生活联合卓越研究中心（LILY））
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
Nanjing University of Science and Technology（南京理工大学）；Hainan University（海南大学）；The Hong Kong Polytechnic University（香港理工大学）；East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）
Federated Learning Incentive Mechanism under Buyers' Auction Market
University of Electronic Science and Technology of China（电子科技大学）；Beihang University（北京航空航天大学）；National Taiwan University（台湾大学）；Jiangxi University of Finance and Economics（江西财经大学）
JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Department of Information Engineering, The Chinese University of Hong Kong（信息工程系，香港中文大学）；School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society（科学与工程学院，深圳人工智能与机器人研究院）；The Chinese University of Hong Kong（香港中文大学）
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation
Duke Kunshan University（昆山杜克大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Yale University（耶鲁大学）
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程学系）
Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives
School of Intelligent Systems Engineering, Sun Yat-Sen University（中山大学智能系统工程学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西智能网络与场景系统重点实验室（信息与通信学院，桂林电子科技大学））；National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology (Guangxi)（综合交通运输大数据应用技术国家工程实验室（广西））；School of Automation, Guangdong University of Technology（自动化学院，广东工业大学）；Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning（广西多媒体通信与网络技术重点实验室，南宁）；School of Computer and Electronic Information, Guangxi University（计算机与电子信息学院，广西大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
1 Department of Multidisciplinary Engineering, Texas A\&M University, College Station, TX, USA；2 Department of Statistics；3 Department of Computer Engineering, KNUST, Kumasi, Ghana；4 Department of Computer Science, George Washington University, Washington, DC, USA；5 Department of Engineering Technology；Industrial Distribution, Texas A\&M University, College Station, TX, USA
Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning
School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin（特里尼蒂学院计算机科学与统计学系，都柏林大学）；School of Electrical and Data Engineering, University of Technology Sydney（技术悉尼大学电气与数据工程系）
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
联邦学习（FL）是涉及多个客户端的新兴机器学习范例，例如，移动电话设备，具有协作解决由中央服务器协调的机器学习问题的激励。FL是由Konečnet al.和McMahan et al.在2016年提出的，作为传统集中式机器学习的一种可行的隐私保护替代方案，因为通过构造，训练数据点是分散的，并且永远不会由客户端传输到中央服务器。因此，FL在一定程度上减轻了与集中式数据汇集相关的隐私风险。不幸的是，FL的优化面临着集中式优化通常不需要处理的几个特定问题。在这篇论文中，我们确定了这些挑战中的一些，并提出了新的方法和算法来解决这些问题，最终目标是使实际的FL解决方案支持数学上严格的保证。
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness
Guangdong University of Technology（广东工业大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Nanyang Technological University（南洋理工大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
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）
