The rapid development of the Internet and smart devices trigger surge in
network traffic making its infrastructure more complex and heterogeneous. The
predominated usage of mobile phones, wearable devices and autonomous vehicles
are examples of distributed networks which generate huge amount of data each
and every day. The computational power of these devices have also seen steady
progression which has created the need to transmit information, store data
locally and drive network computations towards edge devices. Intrusion
detection systems play a significant role in ensuring security and privacy of
such devices. Machine Learning and Deep Learning with Intrusion Detection
Systems have gained great momentum due to their achievement of high
classification accuracy. However the privacy and security aspects potentially
gets jeopardised due to the need of storing and communicating data to
centralized server. On the contrary, federated learning (FL) fits in
appropriately as a privacy-preserving decentralized learning technique that
does not transfer data but trains models locally and transfers the parameters
to the centralized server. The present paper aims to present an extensive and
exhaustive review on the use of FL in intrusion detection system. In order to
establish the need for FL, various types of IDS, relevant ML approaches and its
associated issues are discussed. The paper presents detailed overview of the
implementation of FL in various aspects of anomaly detection. The allied
challenges of FL implementations are also identified which provides idea on the
scope of future direction of research. The paper finally presents the plausible
solutions associated with the identified challenges in FL based intrusion
detection system implementation acting as a baseline for prospective research.

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