Federated Quality Control of Clinical TLF Outputs: Extending TLFQC with Privacy-Preserving Cross-Site Validation for Multi-Center Trials
Keywords:
federated quality control, clinical TLF output validation, privacy-preserving cross-site validation, multi-center clinical trials, differential privacy, secure aggregation, TLFQC, clinical data governanceAbstract
The quality control of Tables, Listings, and Figures (TLFs) in clinical trials remains a critical yet labor-intensive step, particularly in multi-center settings where data privacy regulations restrict the sharing of patient-level information. Existing automated QC platforms, such as TLFQC, offer substantial efficiency gains for single-site operations but lack native support for cross-site validation without compromising data confidentiality. This paper proposes a federated quality control framework that extends the capabilities of TLFQC to enable privacy-preserving cross-site validation for multi-center trials. The architecture leverages a combination of federated learning principles, differential privacy noise injection, and secure aggregation protocols to allow participating sites to collaboratively assess the quality and consistency of TLF outputs without exposing raw clinical data. We discuss the structural trade-offs involved in designing such a system, including the tension between privacy guarantees and validation accuracy, the overhead of cryptographic operations, and the need for harmonized metadata standards across sites. The governance implications of trustless coordination, auditability, and compliance with regulatory frameworks such as HIPAA and GDPR are examined in depth. Deployment considerations, including infrastructure requirements for site-level computing resources and network bandwidth, are addressed through a tiered architecture that accommodates heterogeneous site capabilities. Robustness against adversarial inputs, fairness in quality metrics across diverse patient populations, and long-term sustainability of the federated system are analyzed through cross-domain comparisons with federated learning in medical imaging and electronic health records. A case illustration from an oncology trial demonstrates how the framework can detect systematic discrepancies in adverse event reporting across sites while preserving privacy. The paper concludes with policy recommendations for adopting federated QC in future multi-center trials and outlines directions for extending the approach to real-time monitoring and adaptive quality thresholds.
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