The increasing relevance of distributed data in medical research has accelerated the need for privacy-preserving technologies that enable secure analysis without centralizing sensitive information. Federated learning and analytics have emerged as promising approaches that allow multi-institutional collaboration while maintaining data locality. This paper aims to 1) consolidate key biomedical data types, representative analytical methods, and typical outputs relevant to federated settings across modalities, 2) synthesize which privacy-enhancing technologies (PETs) have been proposed to protect privacy in these contexts by mapping them to distinct FL/FA workflow stages and highlighting stage-specific assumptions and tradeoffs, and 3) develop a modular conceptual framework to structure integration decisions in complex research networks. Drawing on interdisciplinary expertise, we map representative PETs, such as pseudonymization, anonymization, differential privacy, secure computation, and statistical disclosure controls to the Five Safes Framework, encompassing safeguards across trusted users, governance structures, data handling, analytical processes, and outputs. Our synthesis highlights how privacy risks can be mitigated through a layered combination of technical, legal, and organizational measures. The resulting framework supports the modular composition of PETs tailored to specific data protection needs and illustrates how trade-offs between privacy and analytical utility can be balanced. Overall, this work provides a conceptual foundation for integrating PETs in federated biomedical research, aligning technical implementation with compliance and ethical accountability.
2026-03-13
2026-05-15