  In recent times, federated machine learning has been very useful in building
intelligent intrusion detection systems for IoT devices. As IoT devices are
equipped with a security architecture vulnerable to various attacks, these
security loopholes may bring a risk during federated training of decentralized
IoT devices. Adversaries can take control over these IoT devices and inject
false gradients to degrade the global model performance. In this paper, we have
proposed an approach that detects the adversaries with the help of a clustering
algorithm. After clustering, it further rewards the clients for detecting
honest and malicious clients. Our proposed gradient filtration approach does
not require any processing power from the client-side and does not use
excessive bandwidth, making it very much feasible for IoT devices. Further, our
approach has been very successful in boosting the global model accuracy, up to
99% even in the presence of 40% adversaries.
