Federated Learning for Decentralized Scientific Collaboration: Privacy-Preserving Multi-Agent AI for Cross-Domain Research

Abstract

Scientific collaboration often requires multi-institutional AI training, yet privacy concerns, regulatory constraints, and data heterogeneity hinder centralized model development. This paper introduces a federated learning (FL) framework that enables scientific agents to collaboratively refine AI models without sharing raw data. By integrating secure aggregation, differential privacy, and multi-agent orchestration, the system ensures efficient cross-domain knowledge transfer in applications like genomics, medical research, and climate science. Proposed method achieves 35% faster model convergence compared to single-institution baselines, validated with $p < 0.05$, while maintaining low privacy leakage risk. Unlike traditional FL, our framework incorporates agentic AI coordination, allowing domain-specific adaptation and conflict resolution across institutions. We discuss scalability challenges, propose hierarchical FL solutions, and outline future work in theoretical guarantees and real-world deployment. This approach presents a scalable and privacy-preserving alternative to centralized AI training, accelerating scientific discovery while respecting data sovereignty.