The Evolution of Federated Learning-Based Intrusion Detection and Mitigation: A Survey

In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative Machine (ML). FL does not share local data but ML models, offering applications in diverse domains. This paper focuses on application to Intrusion Detection Systems (IDSs). There, common criteria compare existing solutions are missing. particular, this survey shows: (i) how FL-based IDSs used different domains; (ii) what differences exist between architectures; (iii) state art IDS. With a structured literature survey, work identifies relevant FL–based intrusion detection from its creation 2016 until 2021. It provides reference architecture and taxonomy serve as guidelines design IDSs. Both validated with works. Finally, it research directions for systems.