mlsanityData to 14 Oct 2026

Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

arXiv 1810.08553 ↗First posted 19 Oct 2018stat.ML cs.LG q-bio.NCcited by 46

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Abstract (arXiv)

At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, cannot always be shared directly due to privacy and legal concerns, thus limiting the full exploitation of big data in the study of brain disorders. Here we propose a federated learning framework for securely accessing and meta-analyzing any biomedical data without sharing individual information. We illustrate our framework by investigating brain structural relationships across diseases and clinical cohorts. The framework is first tested on synthetic data and then applied to multi-centric, multi-database studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of the approach for further applications in distributed analysis of multi-centric cohorts

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Oct 2018.

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Citing papers per year: 2021: 14; 2022: 7; 2023: 13; 2024: 4; 2025: 5; 2026: 3.

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  1. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  3. 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
  4. 2021Federated Semi-supervised Medical Image Classification via Inter-client Relation Matchingcited by 42
  5. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  6. 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 29
  7. 2023Federated Generative Learning with Foundation Modelscited by 23
  8. 2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 22
  9. 2022IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentationcited by 22
  10. 2023Federated Learning for Computer Visioncited by 22
  11. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  12. 2023Federated Learning for Data Streamscited by 16
34 more
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  2. 2022Federated learning: Applications, challenges and future directionscited by 11
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  4. 2021FedMood: Federated Learning on Mobile Health Data for Mood Detectioncited by 10
  5. 2023FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learningcited by 10
  6. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  7. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  8. 2021A Review on Visual Privacy Preservation Techniques for Active and Assisted Livingcited by 6
  9. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  10. 2022A Comprehensive Survey on Federated Learning: Concept and Applicationscited by 4
  11. 2023SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inferencecited by 4
  12. 2025FedGES: A Federated Learning Approach for BN Structure Learningcited by 4
  13. 2025Spatio-Temporal Representation Decoupling and Enhancement for Federated Instrument Segmentation in Surgical Videoscited by 4
  14. 2023A Comparative Study of Federated Learning Models for COVID-19 Detectioncited by 3
  15. 2024Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systemscited by 3
  16. 2026FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuningcited by 3
  17. 2021Aggregation Delayed Federated Learningcited by 2
  18. 2023Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigationscited by 1
  19. 2023Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignmentcited by 1
  20. 2024FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regionscited by 1
  21. 2025Defending the Edge: Representative-Attention Defense against Backdoor Attacks in Federated Learningcited by 1
  22. 2026DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Trainingcited by 1
  23. 2021FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learningcited by 0
  24. 2021NeuralDP Differentially private neural networks by designcited by 0
  25. 2022Secure & Private Federated Neuroimagingcited by 0
  26. 2022BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcarecited by 0
  27. 2023Covert Communication Based on the Poisoning Attack in Federated Learningcited by 0
  28. 2023Byzantine-Resilient Federated PCA and Low Rank Column-wise Sensingcited by 0
  29. 2023Federated Learning for Sparse Principal Component Analysiscited by 0
  30. 2023OASIS: Offsetting Active Reconstruction Attacks in Federated Learningcited by 0
  31. 2024Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Networkcited by 0
  32. 2024TablePuppet: A Generic Framework for Relational Federated Learningcited by 0
  33. 2025FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Designcited by 0
  34. 2026SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understandingcited by 0