mlsanityData to 14 Oct 2026

The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning

arXiv 2504.08198 ↗First posted 11 Apr 2025cs.LG cs.CRcited by 0

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Abstract (arXiv)

Federated Learning (FL) has been introduced as a way to keep data local to clients while training a shared machine learning model, as clients train on their local data and send trained models to a central aggregator. It is expected that FL will have a huge implication on Mobile Edge Computing, the Internet of Things, and Cross-Silo FL. In this paper, we focus on the widely used FedAvg algorithm to explore the effect of the number of clients in FL. We find a significant deterioration of learning accuracy for FedAvg as the number of clients increases. To address this issue for a general application, we propose a method called Knowledgeable Client Insertion (KCI) that introduces a very small number of knowledgeable clients to the MEC setting. These knowledgeable clients are expected to have accumulated a large set of data samples to help with training. With the help of KCI, the learning accuracy of FL increases much faster even with a normal FedAvg aggregation technique. We expect this approach to be able to provide great privacy protection for clients against security attacks such as model inversion attacks. Our code is available at https://github.com/Eleanor-W/KCI_for_FL.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 11 Apr 2025.

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21 of 27 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2019Deep Leakage from Gradientscited by 1,021
  6. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  7. 2021Model-Contrastive Federated Learningcited by 766
  8. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  9. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  10. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  12. 2021Federated Learning on Non-IID Data: A Surveycited by 346
9 more
  1. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 175
  2. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 75
  3. 2019Federated Learning with Bayesian Differential Privacycited by 60
  4. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  5. 2022Cross-Silo Federated Learning: Challenges and Opportunitiescited by 38
  6. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  7. 2021Personalized Federated Learning over non-IID Data for Indoor Localizationcited by 6
  8. 2023An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneitycited by 5
  9. 2023Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropoutcited by 3

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