mlsanityData to 14 Oct 2026

Fairness in Federated Learning: Trends, Challenges, and Opportunities

arXiv 2509.00799 ↗First posted 31 Aug 2025cs.LG cs.CYcited by 3

Not on the map. Why?

Abstract (arXiv)

At the intersection of the cutting-edge technologies and privacy concerns, Federated Learning (FL) with its distributed architecture, stands at the forefront in a bid to facilitate collaborative model training across multiple clients while preserving data privacy. However, the applicability of FL systems is hindered by fairness concerns arising from numerous sources of heterogeneity that can result in biases and undermine a system's effectiveness, with skewed predictions, reduced accuracy, and inefficient model convergence. This survey thus explores the diverse sources of bias, including but not limited to, data, client, and model biases, and thoroughly discusses the strengths and limitations inherited within the array of the state-of-the-art techniques utilized in the literature to mitigate such disparities in the FL training process. We delineate a comprehensive overview of the several notions, theoretical underpinnings, and technical aspects associated with fairness and their adoption in FL-based multidisciplinary environments. Furthermore, we examine salient evaluation metrics leveraged to measure fairness quantitatively. Finally, we envisage exciting open research directions that have the potential to drive future advancements in achieving fairer FL frameworks, in turn, offering a strong foundation for future research in this pivotal area.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 46 references that are in the map. First posted on arXiv 31 Aug 2025.

Builds on

19 of 46 references.

References · 19

  1. 2014Certifying and removing disparate impactcited by 715
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  4. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  5. 2018Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobservedcited by 94
  6. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  7. 2021Federated Learning with Fair Averagingcited by 61
  8. 2020Mitigating Bias in Federated Learningcited by 56
  9. 2020A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learningcited by 41
  10. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 31
  11. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 29
  12. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
7 more
  1. 2023Fairness and Privacy-Preserving in Federated Learning: A Surveycited by 12
  2. 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
  3. 2021Loss Tolerant Federated Learningcited by 8
  4. 2023BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learningcited by 5
  5. 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
  6. 2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learningcited by 4
  7. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learningcited by 0
  2. 2026Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learningcited by 0
  3. 2026FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attackscited by 0