Privacy Preserved and Practical Implementation for Distributed Machine Learning in Radiology

Imaging is one of the strongest and fastest growing sectors of diagnostic medicine; nevertheless, the incorporation of large-scale medical image datasets across different hospitals is still a challenge due to regulatory, privacy, and infrastructure issues. In this paper, we propose a framework for the application of federated machine learning in radiology and explain how it can be implemented in practice using the Chest X-Ray14 dataset from NIH. We explain how to achieve privacy-preserving data collection and handling in real-world scenarios such as HIPAA and GDPR, secure communication, heterogeneous data preprocessing, and container-based orchestration. The federated approach produces fairly small, but still clinically significant improvements in performance (1.5–2.0% absolute increase in AUC) compared to single-node training and thus proves the feasibility of distributed machine learning in sensitive healthcare environments. We further elaborate on the resource requirements and regulatory constraints and also provide some directions for the future growth of federated radiological analysis so that performance is not overemphasized and the confidentiality of patients is not compromised.