Any attempt that may result in a compromise of information, hence affecting the availability, integrity, or confidentiality of information are considered an intrusion. Intrusion detection systems (IDSs) not only detect such threats; they also effectuate a wide range of response techniques that are implemented in a methodical way relating to the type of attack and tailored to the special requirements of an organization. A strong IDS system should be designed to protect the network and to protect the host system. An IDS monitors the network for tracking unauthorized penetration to be detected with high level of accuracy. In addition, ML-based IDS approaches struggle with managing large datasets with many labels and dimensions. A real-world network with diverse traffic might not be able to use the IDS effectively and efficiently. Federated Learning, therefore, may act as a solution to a variety of issues. This includes enabling highly dynamic, time-critical, and alike applications with data privacy and omnipresent intelligence, high-performance communication with energy-efficient and scalable networking.