Federated Learning for Distributed Threat Intelligence Sharing Across Global Cybersecurity Networks

The rapid evolution of cyber threats necessitates innovative approaches to enhance global
cybersecurity collaboration. Federated Learning (FL) has emerged as a decentralized machine
learning paradigm that enables distributed threat intelligence sharing while maintaining data
privacy and security. This chapter explores the application of FL for large-scale cybersecurity
networks, addressing critical challenges in scalability, security, and communication efficiency.
The focus is on optimizing secure aggregation techniques to enable efficient and privacypreserving model updates across heterogeneous and resource-constrained environments. Key
solutions such as hierarchical aggregation, sparse model updates, and blockchain-based
enhancements are discussed to mitigate the computational and communication overheads inherent
in federated systems. the chapter investigates the integration of advanced cryptographic methods,
including homomorphic encryption and differential privacy, to strengthen the security of federated
networks against adversarial attacks. By leveraging FL’s potential, organizations can share threat
intelligence across global networks without compromising sensitive data, significantly improving
real-time cyber threat detection and response. The chapter concludes by identifying future research
directions for overcoming existing challenges and further optimizing federated models in
cybersecurity.