When aggregating preferences of agents via voting, two desirable goals are to identify outcomes that are Pareto optimal and to incentivize agents to participate in the voting process. We consider participation notions as formalized by Brandl, Brandt, and Hofbauer (2015) and study how far efficiency and participation are achievable by randomized social choice functions in particular when agents' preferences are downward lexicographic (DL) or satisfy stochastic dominance (SD). Our results include the followings ones: we prove formal relations between the participation notions with respect to SD and DL and we show that the maximal recursive rule satisfies very strong participation with respect to both SD and DL.
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）
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）
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
Mechanism Design for Federated Learning with Non-Monotonic Network Effects
Shenzhen Loop Area Institute（深圳河套学院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人社会研究所）；School of Science and Engineering（科学与工程学院）；Data Science Research Center（数据科学研究中心）；Duke Kunshan University（杜克昆山大学）；Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network（深圳 Crowd Intelligence 赋能低碳能源网络重点实验室）；CSIJRI Joint Research Centre on Smart Energy Storage（CSIJRI 智能储能联合研究中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
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）
