mlsanityData to 7 Oct 2026

Scaling Neuroscience Research using Federated Learning

arXiv 2102.08440 ↗First posted 16 Feb 2021cs.LG cs.DCcited by 10

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Abstract (arXiv)

The amount of biomedical data continues to grow rapidly. However, the ability to analyze these data is limited due to privacy and regulatory concerns. Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning is a promising approach to learn a joint model over data silos. This architecture does not share any subject data across sites, only aggregated parameters, often in encrypted environments, thus satisfying privacy and regulatory requirements. Here, we describe our Federated Learning architecture and training policies. We demonstrate our approach on a brain age prediction model on structural MRI scans distributed across multiple sites with diverse amounts of data and subject (age) distributions. In these heterogeneous environments, our Semi-Synchronous protocol provides faster convergence.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

15 of 23 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2020The Future of Digital Health with Federated Learningcited by 625
  6. 2019Federated Machine Learning: Concept and Applicationscited by 409
  7. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  8. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  9. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  10. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  11. 2016Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarkercited by 72
  12. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
3 more
  1. 2017Federated Tensor Factorization for Computational Phenotypingcited by 20
  2. 2019Two-stage Federated Phenotyping and Patient Representation Learningcited by 19
  3. 2020Accelerating Federated Learning in Heterogeneous Data and Computational Environmentscited by 6

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Cited by year

Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 1; 2025: 1.

10 papers.

  1. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  2. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 42
  3. 2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 19
  4. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  5. 2022Towards Sparsified Federated Neuroimaging Models via Weight Pruningcited by 3
  6. 2023Federated attention consistent learning models for prostate cancer diagnosis and Gleason gradingcited by 2
  7. 2023Federated Learning over Harmonized Data Siloscited by 1
  8. 2022Secure & Private Federated Neuroimagingcited by 0
  9. 2024Federated GNNs for EEG-Based Stroke Assessmentcited by 0
  10. 2025Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome predictioncited by 0