mlsanityData to 14 Oct 2026

Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

arXiv 2209.15245 ↗First posted 30 Sep 2022cs.LGcited by 32

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Abstract (arXiv)

Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each communication round. Compared with engaging all the available clients, the random-selection mechanism can lead to significant performance degradation on non-IID (independent and identically distributed) data. In this paper, we show our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from randomly selected clients. Based on our key observation, we design an efficient heterogeneity-aware client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the group dataset from the intentionally selected clients. In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way. Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees. Extensive experimental results demonstrate Fed-CBS outperforms the status quo approaches. Furthermore, it achieves comparable or even better performance than the ideal setting where all the available clients participate in the FL training.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 27 of its 44 references that are in the map. First posted on arXiv 30 Sep 2022.

Builds on

27 of 44 references.

References · 27

  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. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  7. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  8. 2018LEAF: A Benchmark for Federated Settingscited by 792
  9. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 507
  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  11. 2019Cyclical Stochastic Gradient MCMC for Bayesian Deep Learningcited by 203
  12. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
15 more
  1. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 175
  2. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  3. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  4. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  5. 2019Active Federated Learningcited by 63
  6. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 62
  7. 2020Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneitycited by 56
  8. 2020Federated learning with class imbalance reductioncited by 52
  9. 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
  10. 2020Communication-Efficient Federated Learning via Optimal Client Samplingcited by 39
  11. 2018Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theorycited by 20
  12. 2018On the Theory of Variance Reduction for Stochastic Gradient Monte Carlocited by 19
  13. 2020FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learningcited by 14
  14. 2018Variance Reduction in Stochastic Particle-Optimization Samplingcited by 11
  15. 2018Self-Adversarially Learned Bayesian Samplingcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 11; 2025: 10; 2026: 4.

32 papers.

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  1. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 185
  2. 2025A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advancescited by 28
  3. 2024SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Modelscited by 16
  4. 2023FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategycited by 10
  5. 2024FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Datacited by 10
  6. 2025FedMuon: Federated Learning with Bias-corrected LMO-based Optimizationcited by 8
  7. 2023Meta-Reinforcement Learning via Exploratory Task Clusteringcited by 4
  8. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  9. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 3
  10. 2024ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Modelscited by 3
  11. 2024MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learningcited by 3
  12. 2025Federated Client-tailored Adapter for Medical Image Segmentationcited by 3
20 more
  1. 2023FLIPS: Federated Learning using Intelligent Participant Selectioncited by 2
  2. 2023FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropoutcited by 2
  3. 2023Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learningcited by 2
  4. 2024Maverick-Aware Shapley Valuation for Client Selection in Federated Learningcited by 2
  5. 2024SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learningcited by 2
  6. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  7. 2025Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvgcited by 2
  8. 2023LEFL: Low Entropy Client Sampling in Federated Learningcited by 1
  9. 2025FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selectioncited by 1
  10. 2025Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Modelscited by 1
  11. 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learningcited by 1
  12. 2024Navigating High-Degree Heterogeneity: Federated Learning in Aerial and Space Networkscited by 0
  13. 2024FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learningcited by 0
  14. 2024BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networkscited by 0
  15. 2025Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitionscited by 0
  16. 2025Communication-Efficient Federated Learning with Adaptive Number of Participantscited by 0
  17. 2026Federated Vision Transformer with Adaptive Focal Loss for Medical Image Classificationcited by 0
  18. 2026Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognitioncited by 0
  19. 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learningcited by 0
  20. 2026Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learningcited by 0