FLIP-Science: A Federated Learning Framework for Privacy-Preserving Collaborative AI Research

Abstract

The growing reliance on artificial intelligence in scientific research has created a demand for large-scale datasets that institutions hold under strict privacy obligations. Centralized machine learning approaches require data aggregation into a single repository, conflicting with data governance and regulatory requirements. While federated learning supports decentralized model training, existing frameworks focus primarily on algorithm development rather than providing infrastructure for collaborative research workflows. This paper presents FLIP-Science, a federated learning framework that enables privacy-preserving collaborative AI research across multiple institutional nodes. The system introduces an adaptive trust-weighted aggregation mechanism that incorporates client reliability, dataset quality, and communication efficiency to improve model convergence under heterogeneous conditions. Experimental evaluation on standard benchmark datasets demonstrates that FLIP-Science achieves higher accuracy and faster convergence than conventional federated baselines, while maintaining scalable and responsive infrastructure performance suitable for real-world multi-institution deployments.