# Client Selection in Federated Learning: Principles, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2211.01549 · HTML: https://mlsanity.com/paper/2211.01549/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 98 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 30 of 45)

- [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)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Movement Pruning: Adaptive Sparsity by Fine-Tuning](https://mlsanity.com/paper/2005.07683/) (2020)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Not All Samples Are Created Equal: Deep Learning with Importance Sampling](https://mlsanity.com/paper/1803.00942/) (2018)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [Optimal Client Sampling for Federated Learning](https://mlsanity.com/paper/2010.13723/) (2020)
- [Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://mlsanity.com/paper/2004.04314/) (2020)
- [Stochastic Optimization with Importance Sampling](https://mlsanity.com/paper/1401.2753/) (2014)
- [AI Benchmark: All About Deep Learning on Smartphones in 2019](https://mlsanity.com/paper/1910.06663/) (2019)
- [Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge](https://mlsanity.com/paper/2001.10402/) (2020)
- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)

## Built on by (most cited first, 30 of 62)

- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [Multimodal Federated Learning in Healthcare: a Review](https://mlsanity.com/paper/2310.09650/) (2023)
- [Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection](https://mlsanity.com/paper/2401.16685/) (2024)
- [Efficient Asynchronous Federated Learning with Sparsification and Quantization](https://mlsanity.com/paper/2312.15186/) (2023)
- [Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach](https://mlsanity.com/paper/2405.12046/) (2024)
- [CCFC: Bridging Federated Clustering and Contrastive Learning](https://mlsanity.com/paper/2401.06634/) (2024)
- [Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective](https://mlsanity.com/paper/2405.20431/) (2024)
- [Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models](https://mlsanity.com/paper/2501.14406/) (2025)
- [Adaptive Federated Learning via New Entropy Approach](https://mlsanity.com/paper/2303.14966/) (2023)
- [FedD2S: Personalized Data-Free Federated Knowledge Distillation](https://mlsanity.com/paper/2402.10846/) (2024)
- [CCFC++: Enhancing Federated Clustering through Feature Decorrelation](https://mlsanity.com/paper/2402.12852/) (2024)
- [SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments](https://mlsanity.com/paper/2405.04875/) (2024)
- [pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data](https://mlsanity.com/paper/2501.09822/) (2025)
- [Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments](https://mlsanity.com/paper/2506.11054/) (2025)
- [FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning](https://mlsanity.com/paper/2311.06917/) (2023)
- [Maverick-Aware Shapley Valuation for Client Selection in Federated Learning](https://mlsanity.com/paper/2405.12590/) (2024)
- [Data Measurements for Decentralized Data Markets](https://mlsanity.com/paper/2406.04257/) (2024)
- [SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning](https://mlsanity.com/paper/2406.12200/) (2024)
- [FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching](https://mlsanity.com/paper/2504.15366/) (2025)
- [Collaborative Split Federated Learning with Parallel Training and Aggregation](https://mlsanity.com/paper/2504.15724/) (2025)
- [Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning](https://mlsanity.com/paper/2505.11570/) (2025)
- [Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments](https://mlsanity.com/paper/2505.19699/) (2025)
- [AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning](https://mlsanity.com/paper/2506.12754/) (2025)
- [EcoLearn: Optimizing the Carbon Footprint of Federated Learning](https://mlsanity.com/paper/2310.17972/) (2023)
- [Budgeted Online Model Selection and Fine-Tuning via Federated Learning](https://mlsanity.com/paper/2401.10478/) (2024)
- [Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs](https://mlsanity.com/paper/2402.14527/) (2024)
- [KnFu: Effective Knowledge Fusion](https://mlsanity.com/paper/2403.11892/) (2024)
- [IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements](https://mlsanity.com/paper/2404.19485/) (2024)
- [Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning](https://mlsanity.com/paper/2405.13584/) (2024)
- [Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization](https://mlsanity.com/paper/2412.02535/) (2024)

## Abstract (arXiv)

As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia. In a typical FL scenario, clients exhibit significant heterogeneity in terms of data distribution and hardware configurations. Thus, randomly sampling clients in each training round may not fully exploit the local updates from heterogeneous clients, resulting in lower model accuracy, slower convergence rate, degraded fairness, etc. To tackle the FL client heterogeneity problem, various client selection algorithms have been developed, showing promising performance improvement. In this paper, we systematically present recent advances in the emerging field of FL client selection and its challenges and research opportunities. We hope to facilitate practitioners in choosing the most suitable client selection mechanisms for their applications, as well as inspire researchers and newcomers to better understand this exciting research topic.