mlsanityData to 14 Oct 2026

FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction

arXiv 2205.13462 ↗First posted 26 May 2022cs.LGcited by 35

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Abstract (arXiv)

Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order to protect the privacy of clients. This is typically done using local SGD, which helps to improve communication efficiency. However, such a scheme is currently constrained by slow and unstable convergence due to the variety of data on different clients' devices. In this work, we identify three under-explored phenomena of biased local learning that may explain these challenges caused by local updates in supervised FL. As a remedy, we propose FedBR, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges. FedBR has two components. The first component helps to reduce bias in local classifiers by balancing the output of the models. The second component helps to learn local features that are similar to global features, but different from those learned from other data sources. We conducted several experiments to test algopt and found that it consistently outperforms other SOTA FL methods. Both of its components also individually show performance gains. Our code is available at https://github.com/lins-lab/fedbr.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 37 of its 52 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

37 of 52 references.

References · 37

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2015Domain-Adversarial Training of Neural Networkscited by 3,044
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2019Invariant Risk Minimizationcited by 1,854
  6. 2019Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalizationcited by 1,247
  7. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,200
  8. 2020In Search of Lost Domain Generalizationcited by 1,054
  9. 2020Adaptive Federated Optimizationcited by 1,046
  10. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  11. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  12. 2021Model-Contrastive Federated Learningcited by 766
25 more
  1. 2017Learning to Generalize: Meta-Learning for Domain Generalizationcited by 763
  2. 2020Out-of-Distribution Generalization via Risk Extrapolation (REx)cited by 742
  3. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  4. 2020Federated Learning with Matched Averagingcited by 564
  5. 2021Towards Personalized Federated Learningcited by 464
  6. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  7. 2019Agnostic Federated Learningcited by 456
  8. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  9. 2021Escaping the Big Data Paradigm with Compact Transformerscited by 264
  10. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  11. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 202
  12. 2020Improve Unsupervised Domain Adaptation with Mixup Trainingcited by 185
  13. 2020Federated Continual Learning with Weighted Inter-client Transfercited by 161
  14. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 154
  15. 2019Federated Adversarial Domain Adaptationcited by 140
  16. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  17. 2021Distributionally Robust Federated Averagingcited by 104
  18. 2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
  19. 2021FedCM: Federated Learning with Client-level Momentumcited by 77
  20. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  21. 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
  22. 2020Faster Non-Convex Federated Learning via Global and Local Momentumcited by 48
  23. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  24. 2021Towards Federated Learning on Time-Evolving Heterogeneous Datacited by 23
  25. 2021FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptationcited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 12; 2025: 11; 2026: 7.

35 papers.

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  1. 2024Relaxed Contrastive Learning for Federated Learningcited by 32
  2. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 28
  3. 2024FedImpro: Measuring and Improving Client Update in Federated Learningcited by 15
  4. 2025Do We Really Need to Design New Byzantine-robust Aggregation Rules?cited by 15
  5. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  6. 2025FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learningcited by 8
  7. 2024Lightweight Unsupervised Federated Learning with Pretrained Vision Language Modelcited by 5
  8. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  9. 2023Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonizationcited by 4
  10. 2024Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learningcited by 4
  11. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  12. 2023FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentationcited by 3
23 more
  1. 2024Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Siloscited by 3
  2. 2024Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradientscited by 2
  3. 2025FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learningcited by 2
  4. 2025Operator-Theoretic Framework for Gradient-Free Federated Learningcited by 2
  5. 2024FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocationcited by 1
  6. 2025High-Energy Concentration for Federated Learning in Frequency Domaincited by 1
  7. 2025Distribution-Controlled Client Selection to Improve Federated Learning Strategiescited by 1
  8. 2025Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learningcited by 1
  9. 2025H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learningcited by 1
  10. 2025Personalized Collaborative Learning with Affinity-Based Variance Reductioncited by 1
  11. 2024Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexingcited by 0
  12. 2024FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentationcited by 0
  13. 2024An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibrationcited by 0
  14. 2024F-RBA: A Federated Learning-based Framework for Risk-based Authenticationcited by 0
  15. 2025PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Frameworkcited by 0
  16. 2025FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learningcited by 0
  17. 2026When the Server Steps In: Calibrated Updates for Fair Federated Learningcited by 0
  18. 2026Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detectioncited by 0
  19. 2026FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environmentscited by 0
  20. 2026Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clientscited by 0
  21. 2026Federated Active Learning Under Extreme Non-IID and Global Class Imbalancecited by 0
  22. 2026Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guaranteecited by 0
  23. 2026FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgettingcited by 0