A Distributed Deep Learning Approach for Privacy-Preserving Collaborative Healthcare Diagnostics

International Journal of Computer Science and Technology | Vol. 1, No. 11, November 2020 | pp. 81–88

Research Article

Title: A Distributed Deep Learning Approach for Privacy-Preserving Collaborative Healthcare Diagnostics

Names of Authors: Aris Papadopoulos¹, Chloe Nicolaou², and Dimitris Angelopoulos³

Authors’ Affiliations:
¹Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
²Department of Computer Science, University of Cyprus, Nicosia, Cyprus
³School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

Abstract: Deep learning models have demonstrated clinical-grade performance in medical imaging diagnostics, yet training these networks requires massive, highly centralized datasets. In healthcare, centralizing electronic health records or medical scans faces severe regulatory bottlenecks due to strict data privacy legislations like GDPR and HIPAA. This paper proposes a privacy-preserving, distributed deep learning framework based on federated learning (FL) combined with differential privacy (DP) for collaborative disease classification. The proposed system enables multiple independent medical institutions to train a global Convolutional Neural Network (CNN) collaboratively without sharing raw patient data. Each institution computes local model weight updates using its internal dataset, and a central orchestrator aggregates these parameters using the Federated Averaging (FedAvg) algorithm. To thwart membership inference and gradient leakage attacks, a Gaussian noise injection mechanism is integrated into the local gradients based on a rigorous differential privacy budget. Simulations using chest X-ray images for pneumonia detection demonstrate that the proposed framework achieves a diagnostic accuracy of 93.4%, which is within a minor 1.2% margin of the baseline centralized training paradigm. Furthermore, the model successfully maintains data privacy under strong adversarial reconstruction models. The findings demonstrate that combining federated learning with differential privacy provides a secure pathway for large-scale medical AI collaboration without compromising patient confidentiality.

Keywords: Federated learning, Privacy preservation, Differential privacy, Healthcare diagnostics, Collaborative deep learning, Medical imaging

Manuscript Timeline: Received: August 14, 2020; Revised: September 20, 2020; Accepted: October 12, 2020; Published: November 1, 2020

Citation: Papadopoulos, A., Nicolaou, C., & Angelopoulos, D. (2020). A distributed deep learning approach for privacy-preserving collaborative healthcare diagnostics. International Journal of Computer Science and Technology, 1(11), 81–88. DOI: 10.46882/2020/IJCST/000011

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