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Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption

arXiv 2108.03437 ↗First posted 7 Aug 2021cs.CR cs.LGcited by 18

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Abstract (arXiv)

Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location. This results in an improved generalizability of models and efficient scaling of computation as more sources and larger datasets are added to the federation. Nevertheless, recent membership attacks show that private or sensitive personal data can sometimes be leaked or inferred when model parameters or summary statistics are shared with a central site, requiring improved security solutions. In this work, we propose a framework for secure FL using fully-homomorphic encryption (FHE). Specifically, we use the CKKS construction, an approximate, floating point compatible scheme that benefits from ciphertext packing and rescaling. In our evaluation on large-scale brain MRI datasets, we use our proposed secure FL framework to train a deep learning model to predict a person's age from distributed MRI scans, a common benchmarking task, and demonstrate that there is no degradation in the learning performance between the encrypted and non-encrypted federated models.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 7 Aug 2021.

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18 of 43 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2020The Future of Digital Health with Federated Learningcited by 625
  7. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  8. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  9. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
  10. 2016Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarkercited by 72
  11. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  12. 2020Siloed Federated Learning for Multi-Centric Histopathology Datasetscited by 61
6 more
  1. 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 46
  2. 2020Inverse Distance Aggregation for Federated Learning with Non-IID Datacited by 44
  3. 2020POSEIDON: Privacy-Preserving Federated Neural Network Learningcited by 23
  4. 2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 19
  5. 2021Improved Brain Age Estimation with Slice-based Set Networkscited by 11
  6. 2021Scaling Neuroscience Research using Federated Learningcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 6; 2024: 4; 2025: 4; 2026: 1.

18 papers.

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  1. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 121
  2. 2023FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning Systemcited by 38
  3. 2023Federated Learning for Computer Visioncited by 22
  4. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  5. 2023A Privacy-Preserving Federated Learning Approach for Kernel methodscited by 5
  6. 2021Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sourcescited by 4
  7. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  8. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  9. 2023Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networkscited by 1
  10. 2023Federated Learning over Harmonized Data Siloscited by 1
  11. 2025Privacy-Preserving Dataset Combinationcited by 1
  12. 2022Secure & Private Federated Neuroimagingcited by 0
6 more
  1. 2023MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflowscited by 0
  2. 2024On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learningcited by 0
  3. 2025Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversioncited by 0
  4. 2025Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome predictioncited by 0
  5. 2025A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryptioncited by 0
  6. 2026Communication-efficient Federated Graph Classification via Generative Diffusion Modelingcited by 0