Blockchain-Enabled Federated Learning for Secured Edge Data Communication Through a Decentralized Software-Defined Network

Abstract

Data communication through Edge devices in a secured channel for real time applications is one of the biggest concerns. Software Defined Network is a suitable network opted for security. The Control Plane is more vulnerable to variety of attacks. Ensemble machine learning approach which composes of 3 times of Random Forest with one time of Linear Regression gives us the prediction of errors and loss of packets and hence the data will be transferred more securely. The Block Chain is integrated with the SDN controllers in the control plane to transfer the data from the edge devices in the form of a Block to the cloud layer. Federated Learning can be inculcated for security analysis prediction and then aggregating into a Global Model in the Central Cloud Layer. The majority of contemporary FL techniques do not explicitly address variances among client parameter estimates. An aggregation mechanism built on the Hessian matrix in order to close the gap. Moreover, we can use the Hessian as a scaling matrix using the second -order partial derivative information of the loss function