Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification

TL;DR

This paper proposes a differentially private federated learning framework for medical image classification, balancing privacy preservation with model accuracy, and demonstrating the importance of privacy budget calibration.

Contribution

It introduces a novel differentially private federated learning model tailored for medical images, addressing privacy concerns while maintaining classification performance.

Findings

Privacy-accuracy trade-off identified

Effective privacy protection with calibrated privacy budget

Model performance remains robust under privacy constraints

Abstract

The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in nature. Federated learning, which enables cooperative model training without direct data exchange, presents a promising solution. Nevertheless, the inherent vulnerabilities of federated learning necessitate further privacy safeguards. This study addresses this need by integrating differential privacy, a leading privacy-preserving technique, into a federated learning framework for medical image classification. We introduce a novel differentially private federated learning model and meticulously examine its impacts on privacy preservation and model performance. Our research confirms the existence of a trade-off between model accuracy and privacy settings. However, we demonstrate that strategic calibration of the privacy budget in differential privacy can uphold robust image classification performance while providing substantial privacy protection.