mlsanityData to 7 Oct 2026

FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

arXiv 2103.13822 ↗First posted 24 Mar 2021cs.LG cs.DCcited by 43

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Abstract (arXiv)

Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the convergence rate of the FL process. Active client selection strategies are popularly proposed in recent studies. However, they neglect the loss correlations between the clients and achieve only marginal improvement compared to the uniform selection strategy. In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). Based on the GP model, we derive a client selection strategy with a significant reduction of expected global loss in each round. Besides, we develop an efficient GP training method with a low communication overhead in the FL scenario by utilizing the covariance stationarity. Our experimental results show that compared to the state-of-the-art method, FedCorr can improve the convergence rates by 34%~ 99% and 26%~ 51% on FMNIST and CIFAR-10, respectively.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 24 Mar 2021.

Builds on

22 of 37 references.

References · 22

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
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  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  10. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  11. 2017Federated Multi-Task Learningcited by 669
  12. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
10 more
  1. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  2. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  3. 2020Federated Visual Classification with Real-World Data Distributioncited by 126
  4. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  5. 2020Robust Federated Learning: The Case of Affine Distribution Shiftscited by 102
  6. 2019Variance Reduced Local SGD with Lower Communication Complexitycited by 80
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 15; 2024: 7; 2025: 14; 2026: 4.

43 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Drivingcited by 32
  3. 2023MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labelingcited by 25
  4. 2023FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Modelscited by 25
  5. 2023Bayesian Federated Learning: A Surveycited by 22
  6. 2023Federated Learning for Computer Visioncited by 22
  7. 2023Federated Learning under Heterogeneous and Correlated Client Availabilitycited by 18
  8. 2023Fairness-Aware Client Selection for Federated Learningcited by 15
  9. 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
  10. 2023Federated Learning over Coupled Graphscited by 12
  11. 2023Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Modelscited by 10
  12. 2024FedUV: Uniformity and Variance for Heterogeneous Federated Learningcited by 8
31 more
  1. 2022Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Processcited by 5
  2. 2022Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learningcited by 3
  3. 2024Optimizing Federated Learning by Entropy-Based Client Selectioncited by 3
  4. 2025FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learningcited by 3
  5. 2023Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representationscited by 2
  6. 2023Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learningcited by 2
  7. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  8. 2025Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clusteringcited by 2
  9. 2024Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learningcited by 1
  10. 2025Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Controlcited by 1
  11. 2025Energy-Efficient Federated Learning for AIoT using Clustering Methodscited by 1
  12. 2025Distribution-Controlled Client Selection to Improve Federated Learning Strategiescited by 1
  13. 2022FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devicescited by 0
  14. 2023FedCliP: Federated Learning with Client Pruningcited by 0
  15. 2023Personalized Federated Learning with Contextual Modulation and Meta-Learningcited by 0
  16. 2024FLASH: Federated Learning Across Simultaneous Heterogeneitiescited by 0
  17. 2024Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learningcited by 0
  18. 2024GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learningcited by 0
  19. 2024FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learningcited by 0
  20. 2025Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approachcited by 0
  21. 2025UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Datacited by 0
  22. 2025PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learningcited by 0
  23. 2025HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learningcited by 0
  24. 2025CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classificationcited by 0
  25. 2025Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?cited by 0
  26. 2025Local K-Similarity Constraint for Federated Learning with Label Noisecited by 0
  27. 2025Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learningcited by 0
  28. 2026Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognitioncited by 0
  29. 2026Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learningcited by 0
  30. 2026FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Datacited by 0
  31. 2026Personalized Federated Learning via Gaussian Generative Modelingcited by 0