# A Systematic Literature Review on Client Selection in Federated Learning

arXiv: https://arxiv.org/abs/2306.04862 · HTML: https://mlsanity.com/paper/2306.04862/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://mlsanity.com/paper/2004.04314/) (2020)
- [An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee](https://mlsanity.com/paper/2011.01783/) (2020)
- [Energy-Efficient Radio Resource Allocation for Federated Edge Learning](https://mlsanity.com/paper/1907.06040/) (2019)
- [Stochastic Client Selection for Federated Learning with Volatile Clients](https://mlsanity.com/paper/2011.08756/) (2020)
- [Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning](https://mlsanity.com/paper/2012.08009/) (2020)
- [Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT](https://mlsanity.com/paper/2202.01512/) (2022)
- [Context-Aware Online Client Selection for Hierarchical Federated Learning](https://mlsanity.com/paper/2112.00925/) (2021)
- [Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection](https://mlsanity.com/paper/2204.13256/) (2022)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)
- [A Systematic Literature Review on Federated Learning: From A Model Quality Perspective](https://mlsanity.com/paper/2012.01973/) (2020)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)
- [Cognitive Biases in Software Engineering: A Systematic Mapping Study](https://mlsanity.com/paper/1707.03869/) (2017)
- [Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2108.08768/) (2021)
- [Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection](https://mlsanity.com/paper/2109.04253/) (2021)

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

- [Fairness in Federated Learning: Trends, Challenges, and Opportunities](https://mlsanity.com/paper/2509.00799/) (2025)
- [A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach](https://mlsanity.com/paper/2409.11442/) (2024)
- [XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing](https://mlsanity.com/paper/2503.12435/) (2025)

## Abstract (arXiv)

With the arising concerns of privacy within machine learning, federated learning (FL) was invented in 2017, in which the clients, such as mobile devices, train a model and send the update to the centralized server. Choosing clients randomly for FL can harm learning performance due to different reasons. Many studies have proposed approaches to address the challenges of client selection of FL. However, no systematic literature review (SLR) on this topic existed. This SLR investigates the state of the art of client selection in FL and answers the challenges, solutions, and metrics to evaluate the solutions. We systematically reviewed 47 primary studies. The main challenges found in client selection are heterogeneity, resource allocation, communication costs, and fairness. The client selection schemes aim to improve the original random selection algorithm by focusing on one or several of the aforementioned challenges. The most common metric used is testing accuracy versus communication rounds, as testing accuracy measures the successfulness of the learning and preferably in as few communication rounds as possible, as they are very expensive. Although several possible improvements can be made with the current state of client selection, the most beneficial ones are evaluating the impact of unsuccessful clients and gaining a more theoretical understanding of the impact of fairness in FL.