# Fairness in Federated Learning: Trends, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2509.00799 · HTML: https://mlsanity.com/paper/2509.00799/

## 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 (most cited first, 19 of 19)

- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved](https://mlsanity.com/paper/1811.11154/) (2018)
- [FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers](https://mlsanity.com/paper/2010.05958/) (2020)
- [Federated Learning with Fair Averaging](https://mlsanity.com/paper/2104.14937/) (2021)
- [Mitigating Bias in Federated Learning](https://mlsanity.com/paper/2012.02447/) (2020)
- [A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning](https://mlsanity.com/paper/2011.10464/) (2020)
- [A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning](https://mlsanity.com/paper/2201.02932/) (2022)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [A Survey on Efficient Federated Learning Methods for Foundation Model Training](https://mlsanity.com/paper/2401.04472/) (2024)
- [Fairness and Privacy-Preserving in Federated Learning: A Survey](https://mlsanity.com/paper/2306.08402/) (2023)
- [A Survey on Participant Selection for Federated Learning in Mobile Networks](https://mlsanity.com/paper/2207.03681/) (2022)
- [Loss Tolerant Federated Learning](https://mlsanity.com/paper/2105.03591/) (2021)
- [BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning](https://mlsanity.com/paper/2305.05221/) (2023)
- [Federated Fairness Analytics: Quantifying Fairness in Federated Learning](https://mlsanity.com/paper/2408.08214/) (2024)
- [EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2410.02042/) (2024)
- [A Systematic Literature Review on Client Selection in Federated Learning](https://mlsanity.com/paper/2306.04862/) (2023)

## Built on by (most cited first, 3 of 3)

- [Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning](https://mlsanity.com/paper/2608.06469/) (2026)
- [Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning](https://mlsanity.com/paper/2609.12903/) (2026)
- [FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks](https://mlsanity.com/paper/2609.26377/) (2026)

## 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.