mlsanityData to 14 Oct 2026

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

arXiv 2608.02250 ↗First posted 3 Aug 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models. FL enables decentralized training while preserving the privacy of clients' datasets. However, non-independent and identically distributed (non-IID) or noisy datasets can lead to low model accuracy or high convergence latency. Precluding these clients through client selection may mitigate the problem, but heavily biased client selections may also degrade the learning performance. In this study, we first experimentally measure the impact of non-IID data (including skews in data quantity and label distribution), noisy data, and fairness in client selection on model accuracy and convergence. We then propose a privacy-preserving scoring method to assess each client's contribution in FL, with experiments conducted to demonstrate the effectiveness of the proposed assessment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 3 Aug 2026.

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18 of 29 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Learning with Non-IID Datacited by 1,108
  3. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  4. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  5. 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
  6. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 113
  7. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 62
  8. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  9. 2021Towards Fairness-Aware Federated Learningcited by 58
  10. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  11. 2020Robust Federated Learning with Noisy Labelscited by 40
  12. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 39
6 more
  1. 2023Fairness-Aware Client Selection for Federated Learningcited by 15
  2. 2022Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labelscited by 13
  3. 2023FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labelscited by 13
  4. 2024FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clusteringcited by 9
  5. 2022FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learningcited by 7
  6. 2022Toward Data Heterogeneity of Federated Learningcited by 1

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