Federated Learning for Collaborative Network Security in Decentralized Environments

Abstract

In decentralized network environments, collaborative efforts are crucial to bolstering network security against everevolving threats from malicious actors. Federated Learning has emerged as a promising solution, enabling multiple nodes to collectively train machine learning models while preserving data privacy. This research proposes SentinelNet, a novel Federated Learning framework specifically designed for collaborative network security. The framework emphasizes secure threat intelligence sharing, privacy-preserving techniques, and adaptive learning mechanisms. Through comprehensive evaluations and real-world case studies, SentinelNet demonstrates its efficacy in enhancing network security while maintaining data confidentiality. The research highlights the significance of collaborative approaches and advocates the adoption of Federated Learning to fortify decentralized network ecosystems.