August 11, 2025

Implementing federated learning with privacy-preserving encryption to secure patient-derived imaging and sequencing data from cyber intrusions

Key Points

Federated learning enhances data security by enabling training without raw data transfer, preserving privacy.
Integrating homomorphic encryption and differential privacy safeguards patient-derived genomic data from intrusions.
The study explores novel frameworks for federated learning, optimizing for computational efficiency and diagnostic accuracy.
Proposed methods bolster defenses against cyber threats while fostering collaboration in clinical settings.

Abstract

The growing adoption of artificial intelligence (AI) and data-driven analytics in healthcare has accelerated the integration of large-scale patient-derived imaging and genomic sequencing data into clinical workflows. However, this surge in biomedical data sharing has intensified cybersecurity challenges, particularly in protecting sensitive patient information from unauthorized access and cyber intrusions. Traditional centralized machine learning models, which aggregate data into a single repository, pose significant privacy risks and increase the attack surface for malicious actors. To address these challenges, federated learning (FL) has emerged as a transformative paradigm, enabling collaborative model training across decentralized nodes without transferring raw data. Yet, while FL mitigates some privacy concerns, it remains vulnerable to inference attacks, gradient leakage, and model inversion tactics. This paper explores the implementation of federated learning frameworks integrated with privacy-preserving encryption techniques, such as homomorphic encryption, differential privacy, and secure multiparty computation, specifically for safeguarding patient-derived medical imaging and sequencing datasets. These technologies ensure that sensitive genetic markers, radiographic scans, and multi-omic features remain encrypted throughout model training and aggregation processes. We examine recent advances in privacy-enhancing technologies, discuss system architectures suited for cross-institutional healthcare collaboration, and evaluate their performance trade-offs in terms of computational cost, model accuracy, and security guarantees. Furthermore, we propose a hybrid encryption-aware federated learning workflow tailored to radiogenomic applications, highlighting its resilience against adversarial threats while maintaining diagnostic precision. By narrowing focus to clinical implementations, this work provides a scalable and secure foundation for AI-driven biomedical research, enhancing trust and compliance in digital health ecosystems.

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