mlsanityData to 14 Oct 2026

Distribution-Controlled Client Selection to Improve Federated Learning Strategies

arXiv 2509.20877 ↗First posted 25 Sep 2025cs.LGcited by 1

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Abstract (arXiv)

Federated learning (FL) is a distributed learning paradigm that allows multiple clients to jointly train a shared model while maintaining data privacy. Despite its great potential for domains with strict data privacy requirements, the presence of data imbalance among clients is a thread to the success of FL, as it causes the performance of the shared model to decrease. To address this, various studies have proposed enhancements to existing FL strategies, particularly through client selection methods that mitigate the detrimental effects of data imbalance. In this paper, we propose an extension to existing FL strategies, which selects active clients that best align the current label distribution with one of two target distributions, namely a balanced distribution or the federations combined label distribution. Subsequently, we empirically verify the improvements through our distribution-controlled client selection on three common FL strategies and two datasets. Our results show that while aligning the label distribution with a balanced distribution yields the greatest improvements facing local imbalance, alignment with the federation's combined label distribution is superior for global imbalance.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 25 Sep 2025.

Builds on

19 of 35 references.

References · 19

  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. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  6. 2017A systematic study of the class imbalance problem in convolutional neural networkscited by 513
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  8. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  9. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  10. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  11. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  12. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
7 more
  1. 2018Learning Private Neural Language Modeling with Attentive Aggregationcited by 49
  2. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  3. 2022FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reductioncited by 35
  4. 2022Secure Aggregation for Federated Learning in Flowercited by 15
  5. 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
  6. 2022Modeling Global Distribution for Federated Learning with Label Distribution Skewcited by 2
  7. 2023Federated Learning with Neural Graphical Modelscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Adaptive Bayesian Partner Selection for Federated Clinical Centerscited by 0