mlsanityData to 7 Oct 2026

Dopamine: Differentially Private Federated Learning on Medical Data

arXiv 2101.11693 ↗First posted 27 Jan 2021cs.LG cs.CR cs.DCcited by 18

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Abstract (arXiv)

While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (DNNs) for medical diagnostics. We propose Dopamine, a system to train DNNs on distributed datasets, which employs federated learning (FL) with differentially-private stochastic gradient descent (DPSGD), and, in combination with secure aggregation, can establish a better trade-off between differential privacy (DP) guarantee and DNN's accuracy than other approaches. Results on a diabetic retinopathy~(DR) task show that Dopamine provides a DP guarantee close to the centralized training counterpart, while achieving a better classification accuracy than FL with parallel DP where DPSGD is applied without coordination. Code is available at https://github.com/ipc-lab/private-ml-for-health.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 20 of its 44 references that are in the map. First posted on arXiv 27 Jan 2021.

Builds on

20 of 44 references.

References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  4. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  5. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  8. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  9. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  10. 2018Amplification by Shuffling: From Local to Central Differential Privacy via Anonymitycited by 163
  11. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  12. 2017Collecting Telemetry Data Privatelycited by 147
8 more
  1. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  2. 2017Prochlo: Strong Privacy for Analytics in the Crowdcited by 51
  3. 2017Prio: Private, Robust, and Scalable Computation of Aggregate Statisticscited by 35
  4. 2019HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalographycited by 15
  5. 2019Federated and Differentially Private Learning for Electronic Health Recordscited by 13
  6. 2020Guidelines for Implementing and Auditing Differentially Private Systemscited by 10
  7. 2020Anonymizing Data for Privacy-Preserving Federated Learningcited by 8
  8. 2018PySEAL: A Python wrapper implementation of the SEAL homomorphic encryption librarycited by 1

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Cited by year

Citing papers per year: 2021: 7; 2022: 4; 2023: 2; 2024: 4; 2025: 1.

18 papers.

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  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  4. 2021Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputscited by 18
  5. 2021Byzantine-Robust and Privacy-Preserving Framework for FedMLcited by 13
  6. 2022Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attackscited by 12
  7. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  8. 2022Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Datacited by 8
  9. 2022Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learningcited by 8
  10. 2021FLSys: Toward an Open Ecosystem for Federated Learning Mobile Appscited by 6
  11. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  12. 2021Efficient Hyperparameter Optimization for Differentially Private Deep Learningcited by 5
6 more
  1. 2023A Privacy-Preserving Federated Learning Approach for Kernel methodscited by 5
  2. 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directionscited by 4
  3. 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 4
  4. 2024Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Datacited by 3
  5. 2024On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Stepscited by 1
  6. 2021Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scanscited by 0