mlsanityData to 7 Oct 2026

MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare

arXiv 2206.08516 ↗First posted 17 Jun 2022cs.LG cs.CYcited by 36

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Abstract (arXiv)

Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare. In real applications, different federations can seldom work together due to possible reasons such as data heterogeneity and distrust/inexistence of the central server. In this paper, we propose a novel framework called MetaFed to facilitate trustworthy FL between different federations. MetaFed obtains a personalized model for each federation without a central server via the proposed Cyclic Knowledge Distillation. Specifically, MetaFed treats each federation as a meta distribution and aggregates knowledge of each federation in a cyclic manner. The training is split into two parts: common knowledge accumulation and personalization. Comprehensive experiments on three benchmarks demonstrate that MetaFed without a server achieves better accuracy compared to state-of-the-art methods (e.g., 10%+ accuracy improvement compared to the baseline for PAMAP2) with fewer communication costs.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 28 of its 70 references that are in the map. First posted on arXiv 17 Jun 2022.

Builds on

28 of 70 references.

References · 28

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2014FitNets: Hints for Thin Deep Netscited by 1,619
  5. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  6. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  7. 2020The Future of Digital Health with Federated Learningcited by 625
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  9. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  10. 2021Towards Personalized Federated Learningcited by 462
  11. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  12. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
16 more
  1. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  2. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  3. 2017Deep Learning for Sensor-based Activity Recognition: A Surveycited by 133
  4. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  5. 2021Medical Image Segmentation Using Squeeze-and-Expansion Transformerscited by 56
  6. 2021A distillation-based approach integrating continual learning and federated learning for pervasive servicescited by 42
  7. 2021Towards Model Agnostic Federated Learning Using Knowledge Distillationcited by 31
  8. 2022Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognitioncited by 28
  9. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  10. 2021Cross-domain Activity Recognition via Substructural Optimal Transportcited by 21
  11. 2020FedFMC: Sequential Efficient Federated Learning on Non-iid Datacited by 17
  12. 2021FedH2L: Federated Learning with Model and Statistical Heterogeneitycited by 17
  13. 2022Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Trainingcited by 7
  14. 2022Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclientscited by 6
  15. 2021Locality-aware Channel-wise Dropout for Occluded Face Recognitioncited by 5
  16. 2020DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryptioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 15; 2025: 9; 2026: 3.

36 papers.

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  1. 2023FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learningcited by 70
  2. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  3. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  4. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  5. 2024Beyond the Hype: A dispassionate look at vision-language models in medical scenariocited by 10
  6. 2024One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversitycited by 8
  7. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 8
  8. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  9. 2024FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentationcited by 6
  10. 2023Knowledge Distillation in Federated Edge Learning: A Surveycited by 5
  11. 2023Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregationcited by 4
  12. 2024F3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristicscited by 4
24 more
  1. 2023Rethinking Client Drift in Federated Learning: A Logit Perspectivecited by 3
  2. 2024RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protectioncited by 3
  3. 2024Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memorycited by 3
  4. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  5. 2024Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decouplingcited by 2
  6. 2024Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalizationcited by 2
  7. 2025MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Taskscited by 2
  8. 2023Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clientscited by 1
  9. 2024Fairness-Aware Job Scheduling for Multi-Job Federated Learningcited by 1
  10. 2024KnFu: Effective Knowledge Fusioncited by 1
  11. 2024FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learningcited by 0
  12. 2024FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Datacited by 0
  13. 2024Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shiftscited by 0
  14. 2025Task-Agnostic Federation over Decentralized Data: Research Landscape and Visionscited by 0
  15. 2025A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problemscited by 0
  16. 2025FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcarecited by 0
  17. 2025FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learningcited by 0
  18. 2025A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learningcited by 0
  19. 2025Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Datacited by 0
  20. 2025SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillationcited by 0
  21. 2025Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusioncited by 0
  22. 2026FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decouplingcited by 0
  23. 2026Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Datacited by 0
  24. 2026Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Predictioncited by 0