Meta-Analysis and Federated Learning over Decentralized Distributed Research Networks
Abstract
Distributed research networks have transformed modern clinical research by enabling large-scale, multi-institutional collaborations while maintaining patient privacy. Two prominent methodologies within these frameworks-meta-analysis and federated learning-address the challenges of synthesizing evidence from decentralized data. Meta-analysis aggregates study-level results to provide robust, interpretable estimates, making it a cornerstone of evidence synthesis for association studies. Federated learning complements this by enabling complex downstream tasks, such as predictive modeling and counterfactual inference, while preserving data privacy through privacy-preserving distributed algorithms. Federated learning facilitates communication-efficient computation and adapts seamlessly to heterogeneous datasets across diverse institutions. This review emphasizes the complementary strengths of federated learning's scalability, flexibility, and readiness for implementation alongside meta-analysis's robust frameworks for evidence synthesis and aggregation in clinical research. Integrations of synthetic data, artificial intelligence (AI)-enhanced harmonization, and hybrid human-AI frameworks are proposed as future directions, promising to further advance both methodologies and enhance their combined impact on privacy-conscious, data-driven healthcare research.
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