# A Snapshot of the Frontiers of Client Selection in Federated Learning

arXiv: https://arxiv.org/abs/2210.04607 · HTML: https://mlsanity.com/paper/2210.04607/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 75 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 30 of 35)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (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)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [Optimal Client Sampling for Federated Learning](https://mlsanity.com/paper/2010.13723/) (2020)
- [LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning](https://mlsanity.com/paper/1805.09965/) (2018)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [Measure Contribution of Participants in Federated Learning](https://mlsanity.com/paper/1909.08525/) (2019)
- [Active Federated Learning](https://mlsanity.com/paper/1909.12641/) (2019)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Federated learning with class imbalance reduction](https://mlsanity.com/paper/2011.11266/) (2020)
- [FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC](https://mlsanity.com/paper/2002.09699/) (2020)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://mlsanity.com/paper/1908.03092/) (2019)
- [Anarchic Federated Learning](https://mlsanity.com/paper/2108.09875/) (2021)
- [FedCoin: A Peer-to-Peer Payment System for Federated Learning](https://mlsanity.com/paper/2002.11711/) (2020)
- [Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://mlsanity.com/paper/1912.06370/) (2019)
- [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021)
- [No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices](https://mlsanity.com/paper/2202.08036/) (2022)
- [Game of Gradients: Mitigating Irrelevant Clients in Federated Learning](https://mlsanity.com/paper/2110.12257/) (2021)

## Built on by (most cited first, 9 of 9)

- [Enhancing Privacy in Federated Learning through Local Training](https://mlsanity.com/paper/2403.17572/) (2024)
- [A Framework for Incentivized Collaborative Learning](https://mlsanity.com/paper/2305.17052/) (2023)
- [Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning](https://mlsanity.com/paper/2311.17750/) (2023)
- [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)
- [Adaptive Client Selection with Personalization for Communication Efficient Federated Learning](https://mlsanity.com/paper/2411.17833/) (2024)
- [FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection](https://mlsanity.com/paper/2504.11216/) (2025)
- [Near-optimal Differentially Private Client Selection in Federated Settings](https://mlsanity.com/paper/2310.09370/) (2023)
- [Communication-Efficient Learning for Satellite Constellations](https://mlsanity.com/paper/2511.20220/) (2025)

## Abstract (arXiv)

Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model's parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as a strategy to limit the number of communicating parties at every step of the process. Since the early na"ive random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.