FEDERATED LEARNING FOR THREAT INTELLIGENCE SHARING: A PRIVACY-PRESERVING COLLABORATIVE DEFENSE MODEL

Abstract

The emergence of advanced, cross-national cyber threats is a reason that promotes the sharing of threat intelligence, which is often hampered due to the law of privacy, security interests, and political conflicts. Older centralized systems in which sensitive information is aggregated in one repository are becoming unsustainable. Federated Learning (FL) provides a paradigm shift and allows jointly training machine learning models by providing no raw data to any of the entities. This decentralized model lets the cross-border organizations enjoy a collective defense model without compromising their confidential information rights. In this paper, the design, implementation, and challenges of FL systems in achieving cybersecurity are discussed. It outlines architectural designs, privacy protection mechanisms, such as differential privacy and homomorphic encryption, and gives financial, critical infrastructure, and national security applications. The outcomes of the simulations reveal a reasonable privacy-utility trade-off and the paper ends with a roadmap to future research and real-life implementation, in which FL can serve as the foundation of contemporary, privacy-conscious cyber defense.