mlsanityData to 7 Oct 2026

Federated learning with class imbalance reduction

arXiv 2011.11266 ↗First posted 23 Nov 2020cs.LG cs.AI cs.DCcited by 52

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Abstract (arXiv)

Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to privacy concerns, the raw data on devices could not be available for centralized server. Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged to train and transmit the trained model to centralized server for aggregation. Since the local data distribution varies among all devices, class imbalance problem arises along with the unfavorable client selection, resulting in a slow converge rate of the global model. In this paper, an estimation scheme is designed to reveal the class distribution without the awareness of raw data. Based on the scheme, a device selection algorithm towards minimal class imbalance is proposed, thus can improve the convergence performance of the global model. Simulation results demonstrate the effectiveness of the proposed algorithm.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Nov 2020.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 14; 2024: 14; 2025: 7.

52 papers.

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  1. 2021Towards Personalized Federated Learningcited by 462
  2. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  3. 2022Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Featurescited by 63
  4. 2021Towards Fairness-Aware Federated Learningcited by 57
  5. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  6. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39
  7. 2022Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reductioncited by 32
  8. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  9. 2022Fairness in Federated Learning via Core-Stabilitycited by 22
  10. 2022FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classificationcited by 18
  11. 2021On Bridging Generic and Personalized Federated Learning for Image Classificationcited by 15
  12. 2023A Survey on Class Imbalance in Federated Learningcited by 12
38 more
  1. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
  2. 2024FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Datacited by 10
  3. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  4. 2023CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Datacited by 9
  5. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  6. 2024Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approachcited by 8
  7. 2021An Experimental Study of Class Imbalance in Federated Learningcited by 7
  8. 2023A Survey of Federated Evaluation in Federated Learningcited by 7
  9. 2021Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedbackcited by 6
  10. 2023FederatedTrust: A Solution for Trustworthy Federated Learningcited by 5
  11. 2021Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradientcited by 4
  12. 2022Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learningcited by 4
  13. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 4
  14. 2024ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Modelscited by 3
  15. 2024MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learningcited by 3
  16. 2021Robust Convergence in Federated Learning through Label-wise Clusteringcited by 2
  17. 2023Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learningcited by 2
  18. 2023A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANscited by 2
  19. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
  20. 2024Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalizationcited by 2
  21. 2025AFed: Algorithmic Fair Federated Learningcited by 2
  22. 2021Demystifying the Effects of Non-Independence in Federated Learningcited by 1
  23. 2022Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasetscited by 1
  24. 2023Communication Trade-offs in Federated Learning of Spiking Neural Networkscited by 1
  25. 2023Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variancecited by 1
  26. 2023LEFL: Low Entropy Client Sampling in Federated Learningcited by 1
  27. 2024Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offscited by 1
  28. 2025PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learningcited by 1
  29. 2025Towards Unbiased Federated Graph Learning: Label and Topology Perspectivescited by 1
  30. 2022FedVeca: Federated Vectorized Averaging on Non-IID Data with Adaptive Bi-directional Global Objectivecited by 0
  31. 2023Addressing Class Variable Imbalance in Federated Semi-supervised Learningcited by 0
  32. 2023Utilizing Free Clients in Federated Learning for Focused Model Enhancementcited by 0
  33. 2024Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradientscited by 0
  34. 2024Class-Wise Federated Averaging for Efficient Personalizationcited by 0
  35. 2024Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learningcited by 0
  36. 2024On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learningcited by 0
  37. 2024BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networkscited by 0
  38. 2025Drift-Aware Federated Learning: A Causal Perspectivecited by 0

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