Blockchain Enabled Federated Learning for Detection of Malicious Internet of Things Nodes

The Internet of Things (IoTs) networks are evolving day by day as they have been used in almost every field of life in the last few decades. The reason for the increasing trend of IoT networks is due to the increasing population of the world. However, these networks are vulnerable to the presence of malicious nodes, which compromise the efficiency of the decision-making process in the IoT network. Many machine learning and artificial intelligence techniques are proposed to solve this issue. Centralized learning is performed in these conventional machine learning techniques due to which the privacy of the network is compromised. Therefore, internal users are not encouraged to share their sensitive information in the network and external users do not want to join and rely on such a trustless environment. To solve these issues, we propose a mechanism in which the distributed model training is performed for detecting malicious nodes. The distributed models are trained and then a unified model is generated at the centralized server. This will not only enhance the accuracy of the unified federated learning model but also preserve the privacy of each cluster because no actual data is sent to fog layer for central model training. We simulate the whole IoT network and for evaluating the performance of our proposed model. The simulation results show that an accuracy of 79% is achieved by our model, indicating that the malicious node is efficiently detected. Furthermore, the precision of our model is 1, which indicates that our model can easily discriminate between the true and false classes.