Dopamine: Differentially Private Federated Learning on Medical Data

2021/01/27 by Mohammad Malekzadeh, Malekzadeh, Mohammad, Burak Hasırcıoglu +9 · 2 citations
Computer Science · #Cryptography and Data Security #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2101.11693

openalex publication_date 2021/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• On lattices, learning with errors, random linear codes, and cryptography
• Prio: Private, Robust, and Scalable Computation of Aggregate Statistics
• HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
• Guidelines for Implementing and Auditing Differentially Private Systems
• Anonymizing Data for Privacy-Preserving Federated Learning

Cited by

• Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges
• Federated Learning for Cyber Physical Systems: A Comprehensive Survey

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