mlsanityData to 7 Oct 2026

Personalized Federated Learning with Adaptive Batchnorm for Healthcare

arXiv 2112.00734 ↗First posted 1 Dec 2021cs.LG cs.CVcited by 26

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Abstract (arXiv)

There is a growing interest in applying machine learning techniques to healthcare. Recently, federated learning (FL) is gaining popularity since it allows researchers to train powerful models without compromising data privacy and security. However, the performance of existing FL approaches often deteriorates when encountering non-iid situations where there exist distribution gaps among clients, and few previous efforts focus on personalization in healthcare. In this article, we propose FedAP to tackle domain shifts and then obtain personalized models for local clients. FedAP learns the similarity between clients based on the statistics of the batch normalization layers while preserving the specificity of each client with different local batch normalization. Comprehensive experiments on five healthcare benchmarks demonstrate that FedAP achieves better accuracy compared to state-of-the-art methods (e.g., 10% accuracy improvement for PAMAP2) with faster convergence speed.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 37 of its 75 references that are in the map. First posted on arXiv 1 Dec 2021.

Builds on

37 of 75 references.

References · 37

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  5. 2019Deep Leakage from Gradientscited by 1,004
  6. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  9. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  10. 2019Federated Learning with Personalization Layerscited by 533
  11. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  12. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
25 more
  1. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  2. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  3. 2020Adaptive Personalized Federated Learningcited by 353
  4. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  5. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  6. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  7. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 320
  8. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  9. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  10. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  11. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  12. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  13. 2020Federated Continual Learning with Weighted Inter-client Transfercited by 158
  14. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 157
  15. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  16. 2020Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applicationscited by 79
  17. 2018MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcarecited by 72
  18. 2021Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Datacited by 69
  19. 2021Provably Secure Federated Learning against Malicious Clientscited by 60
  20. 2020Batch Normalization Embeddings for Deep Domain Generalizationcited by 57
  21. 2020Inverse Distance Aggregation for Federated Learning with Non-IID Datacited by 44
  22. 2021Cross-domain Activity Recognition via Substructural Optimal Transportcited by 21
  23. 2018Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcarecited by 10
  24. 2019Federated Learning for Healthcare Informaticscited by 6
  25. 2020ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcarecited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 10; 2024: 7; 2025: 4; 2026: 2.

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  1. 2023FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learningcited by 70
  2. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 36
  3. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  4. 2024Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domainscited by 27
  5. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  6. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  7. 2023FedWon: Triumphing Multi-domain Federated Learning Without Normalizationcited by 11
  8. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  9. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  10. 2023pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learningcited by 9
  11. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  12. 2023Making Batch Normalization Great in Federated Deep Learningcited by 8
14 more
  1. 2023Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learningcited by 7
  2. 2024FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentationcited by 6
  3. 2022Federated Continual Learning for Socially Aware Roboticscited by 5
  4. 2023Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregationcited by 4
  5. 2024F3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristicscited by 4
  6. 2025Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontierscited by 3
  7. 2023A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directionscited by 2
  8. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  9. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  10. 2025Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformationscited by 1
  11. 2025Bayesian Coreset Optimization for Personalized Federated Learningcited by 1
  12. 2026Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysiscited by 1
  13. 2025FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcarecited by 0
  14. 2026Few-for-Many Personalized Federated Learningcited by 0