# Fairness-Aware Client Selection for Federated Learning

arXiv: https://arxiv.org/abs/2307.10738 · HTML: https://mlsanity.com/paper/2307.10738/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2109.02053/) (2021)
- [An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee](https://mlsanity.com/paper/2011.01783/) (2020)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Hard Sample Aware Noise Robust Learning for Histopathology Image Classification](https://mlsanity.com/paper/2112.03694/) (2021)
- [FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning](https://mlsanity.com/paper/2103.13822/) (2021)
- [Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data](https://mlsanity.com/paper/1905.07210/) (2019)
- [Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data](https://mlsanity.com/paper/2203.15321/) (2022)
- [Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning](https://mlsanity.com/paper/2012.08009/) (2020)
- [Hierarchically Fair Federated Learning](https://mlsanity.com/paper/2004.10386/) (2020)

## Built on by (most cited first, 15 of 15)

- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [Intelligent Agents for Auction-based Federated Learning: A Survey](https://mlsanity.com/paper/2404.13244/) (2024)
- [Multi-Session Budget Optimization for Forward Auction-based Federated Learning](https://mlsanity.com/paper/2311.12548/) (2023)
- [Fairness-Aware Job Scheduling for Multi-Job Federated Learning](https://mlsanity.com/paper/2401.02740/) (2024)
- [Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning](https://mlsanity.com/paper/2405.13584/) (2024)
- [Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients](https://mlsanity.com/paper/2504.12577/) (2025)
- [PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning](https://mlsanity.com/paper/2506.15923/) (2025)
- [HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning](https://mlsanity.com/paper/2508.06692/) (2025)
- [FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks](https://mlsanity.com/paper/2509.20193/) (2025)
- [Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?](https://mlsanity.com/paper/2511.01737/) (2025)
- [FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning](https://mlsanity.com/paper/2511.15454/) (2025)
- [Federated Learning at the Forefront of Fairness: A Multifaceted Perspective](https://mlsanity.com/paper/2602.00718/) (2026)
- [Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation](https://mlsanity.com/paper/2602.13651/) (2026)
- [SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System](https://mlsanity.com/paper/2606.18384/) (2026)
- [Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning](https://mlsanity.com/paper/2608.02250/) (2026)

## Abstract (arXiv)

Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server can only engage a limited number of clients in each training round, FL client selection has become an important research problem. Existing approaches generally focus on either enhancing FL model performance or enhancing the fair treatment of FL clients. The problem of balancing performance and fairness considerations when selecting FL clients remains open. To address this problem, we propose the Fairness-aware Federated Client Selection (FairFedCS) approach. Based on Lyapunov optimization, it dynamically adjusts FL clients' selection probabilities by jointly considering their reputations, times of participation in FL tasks and contributions to the resulting model performance. By not using threshold-based reputation filtering, it provides FL clients with opportunities to redeem their reputations after a perceived poor performance, thereby further enhancing fair client treatment. Extensive experiments based on real-world multimedia datasets show that FairFedCS achieves 19.6% higher fairness and 0.73% higher test accuracy on average than the best-performing state-of-the-art approach.