Federated learning (FL) trains a shared model from updates contributed by distributed clients, often implicitly assuming that contributing clients are representative of the target population. In practice, this representativeness assumption can fail at two distinct stages, inducing selection bias. First, eligibility rules such as device constraints, software requirements, or user consent determine which clients are ever enrolled and reachable for training, inducing \emph{enrollment bias}. Second, among enrolled clients, user and system factors such as battery state, network status, and local time determine which clients participate in each communication round, inducing \emph{participation bias}. Although existing work has largely addressed round-level participation bias, it has paid far less attention to population-level enrollment bias, which can induce a persistent mismatch between the training objective and the target-population objective. We formalize FL under a two-stage selection model and derive \textsc{FedIPW}, an inverse-probability-weighted aggregation scheme that recovers the target-population mean update under standard ignorability and positivity assumptions. Because client-level covariates are often unavailable for non-enrolled clients, we also introduce a limited-information aggregate-calibration extension that uses known target-population summaries to reweight the enrolled sample, partially correcting enrollment bias. We further provide an algorithm-agnostic optimization analysis under residual weighting error and show that incomplete selection correction can induce a non-vanishing bias floor. Finally, experiments on synthetic federated logistic regression validate the predicted objective mismatch and show that enrollment correction reduces target-population error under two-stage selection.
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）
DELTA: Diverse Client Sampling for Fasting Federated Learning
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）；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（西湖大学工学院）；The Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来智能网络重点实验室）
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
Elmore Family School of Electrical and Computer Engineering, Purdue University（普渡大学埃尔莫尔家族电气与计算机工程学院）；University at Buffalo (SUNY)（纽约州立大学布法罗分校）；University of Florida（佛罗里达大学）；School of Engineering Education, Purdue University（普渡大学工程教育学院）；Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）
Fairness-Aware Federated Learning with Trajectory Shapley Value
Faculty of Mathematics, Ecole Normale Supérieure Paris-Saclay（巴黎-萨克雷大学数学系）；Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔兰根-纽伦堡弗里德里希-亚历山大大学数学系）
On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach
School of Computer Technologies, RMIT University（RMIT大学计算机技术学院）；School of Science, Computing and Engineering Technologies, Swinburne University of Technology（斯威丁大学科学与工程技术学院）
FedProf: Selective Federated Learning with Representation Profiling
Department of Computer Science, University of Warwick（华威大学计算机科学系）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Warwick Manufacturing Group (WMG), University of Warwick（华威大学华威制造集团(WMG)）；University of Warwick（华威大学）；South China University of Technology（华南理工大学）
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Shanghai AI Lab（上海人工智能实验室）；George Washington University（乔治华盛顿大学）；Ryerson University（瑞尔森大学）
SA-PEF: Step-Ahead Partial Error Feedback for Efficient Federated Learning
Department of Electronic Systems, Norwegian University of Science and Technology (NTNU)（挪威科学技术大学电子系统系）；Department of Information and Communications Engineering, Aalto University（阿尔托大学信息与通信工程系）；CSIRO’s Data61（CSIRO数据61）
Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling
School of Science and Engineering, the Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工学院）；Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电气与电子工程系）
