Federated Learning for Real-Time Network Intrusion Detection: Towards Privacy, Scalability, and Robustness in Decentralized Cybersecurity

There must be more innovative, scalable, and privacy-preserving ways to protect ourselves against the growing sophistication of cyberattacks. Some specific problems we are trying to solve include centralized architectures prone to data breaches, high latencies, and single point(s) of failures in traditional network intrusion detection systems (NIDS). In this ongoing research paper, we present a new decentralized architecture for a real-time Network Intrusion Detection System (NIDS) based on Federated Learning (FL), a mechanism whereby a large number of sites cooperate to create a model through the training of their local data without ever sharing it to guarantee privacy. In contrast to classical methods, FL offers a model trained across thousands of devices (and the associated data) in a decentralized computing framework, thus enhancing detection and robustness under changing attack patterns. However, to work in the real world, some challenges — such as communication overhead, data heterogeneity, and adversarial model poisoning- still need to be overcome. These cover secure FL aggregation for scalability, lightweight model architectures for scalable FL, and efficient FL strategies with adversarial robustness. The preliminary findings show that while centralized approaches struggle to keep up with new threats and preserve privacy, FL-based NIDS performs better. Future works are focused on enhancing real-time processing capabilities and scaling non-numerical data secure, ultra-scalable FL frameworks for executing massive-scale cybersecurity applications.