Federated Learning at the Forefront of Fairness: A Multifaceted Perspective
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Abstract (arXiv)
Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients' constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive classification of the state-of-the-art fairness-aware approaches from a multifaceted perspective, i.e., model performance-oriented and capability-oriented. Moreover, we provide a framework to categorize and address various fairness concerns and associated technical aspects, examining their effectiveness in balancing equity and performance within FL frameworks. We further examine several significant evaluation metrics leveraged to measure fairness quantitatively. Finally, we explore exciting open research directions and propose prospective solutions that could drive future advancements in this important area, laying a solid foundation for researchers working toward fairness in FL.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 44 references that are in the map. First posted on arXiv 31 Jan 2026.
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31 of 44 references.
References · 31
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
- 2019Agnostic Federated Learningcited by 451
- 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
- 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
- 2020Collaborative Fairness in Federated Learningcited by 95
- 2020Fairness-aware Agnostic Federated Learningcited by 87
- 2020On the Privacy Risks of Algorithmic Fairnesscited by 82
- 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
- 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 61
- 2021Federated Learning with Fair Averagingcited by 59
19 more
- 2021Towards Fairness-Aware Federated Learningcited by 57
- 2019Average Individual Fairness: Algorithms, Generalization and Experimentscited by 46
- 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
- 2022Disparate Impact in Differential Privacy from Gradient Misalignmentcited by 31
- 2022On Learning Fairness and Accuracy on Multiple Subgroupscited by 31
- 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
- 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 28
- 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 28
- 2023Confidence-aware Personalized Federated Learning via Variational Expectation Maximizationcited by 17
- 2024Redefining Contributions: Shapley-Driven Federated Learningcited by 16
- 2021Collaboration Equilibrium in Federated Learningcited by 15
- 2023Fairness-Aware Client Selection for Federated Learningcited by 15
- 2024Utility-Fairness Trade-Offs and How to Find Themcited by 15
- 2023Fairness and Privacy-Preserving in Federated Learning: A Surveycited by 12
- 2023A Survey of Federated Evaluation in Federated Learningcited by 7
- 2023Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learningcited by 5
- 2023BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learningcited by 5
- 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
- 2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learningcited by 4
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