Federated Learning and Network Security: Foundations, Potential, and Resilience

Federated Learning (FL) is a distributed learning paradigm that enables training models across distributed clients without accessing their data. In the context of network security, FL can be used to collaboratively train Intrusion Detection System (IDS) models across multiple organizations, allowing participants to share knowledge without compromising data privacy. However, the distributed nature of FL raises new challenges, notably the heterogeneity of clients' data distributions and the identification of malicious contributions. This three-part tutorial introduces the audience to (i) the principles of FL, (ii) its application to network security, focusing on building Collaborative Intrusion Detection Systems (CIDSs) using FL, and (iii) the security challenges associated with deploying Federated Intrusion Detection System (FIDS), with a focus on poisoning attacks. Each part is illustrated with hands-on exercises, with step-by-step instructions provided in the companion material.