  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.
