mlsanityData to 14 Oct 2026

A Snapshot of the Frontiers of Client Selection in Federated Learning

arXiv 2210.04607 ↗First posted 27 Sep 2022cs.DC cs.AI cs.CRcited by 9

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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.

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

35 of 75 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2018LEAF: A Benchmark for Federated Settingscited by 792
  7. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 544
  8. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  9. 2019Agnostic Federated Learningcited by 456
  10. 2019Fair Resource Allocation in Federated Learningcited by 394
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  12. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 337
23 more
  1. 2021A Field Guide to Federated Optimizationcited by 315
  2. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 163
  3. 2020Optimal Client Sampling for Federated Learningcited by 124
  4. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 96
  5. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  6. 2019Measure Contribution of Participants in Federated Learningcited by 79
  7. 2019Active Federated Learningcited by 63
  8. 2021Towards Fairness-Aware Federated Learningcited by 58
  9. 2020Federated learning with class imbalance reductioncited by 52
  10. 2020FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MECcited by 48
  11. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  12. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  13. 2021Anarchic Federated Learningcited by 42
  14. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  15. 2019Toward an Automated Auction Framework for Wireless Federated Learning Services Marketcited by 38
  16. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  17. 2022No One Left Behind: Inclusive Federated Learning over Heterogeneous Devicescited by 37
  18. 2021Game of Gradients: Mitigating Irrelevant Clients in Federated Learningcited by 35
  19. 2020Trading Data For Learning: Incentive Mechanism For On-Device Federated Learningcited by 21
  20. 2021Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversarycited by 14
  21. 2021Federated Learning Meets Fairness and Differential Privacycited by 9
  22. 2020Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learningcited by 8
  23. 2021Loss Tolerant Federated Learningcited by 8

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 3; 2025: 2.

9 papers.

  1. 2024Enhancing Privacy in Federated Learning through Local Trainingcited by 3
  2. 2023A Framework for Incentivized Collaborative Learningcited by 2
  3. 2023Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learningcited by 2
  4. 2023EcoLearn: Optimizing the Carbon Footprint of Federated Learningcited by 1
  5. 2024Budgeted Online Model Selection and Fine-Tuning via Federated Learningcited by 1
  6. 2024Adaptive Client Selection with Personalization for Communication Efficient Federated Learningcited by 1
  7. 2025FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selectioncited by 1
  8. 2023Near-optimal Differentially Private Client Selection in Federated Settingscited by 0
  9. 2025Communication-Efficient Learning for Satellite Constellationscited by 0