PERSONALIZED FEDERATED LEARNING METHODS FOR INDUSTRIAL INTERNET OF THINGS TARGETING CLIENT NEEDS

Abstract

A personalized federated learning method for industrial Internet of Things targeting client needs is provided. The method includes: issuing, by a server, an initial model to each client as a local model, the local model including a shared layer and a personalized layer; for each client, freezing parameters of the personalized layer and locally training the shared layer based on local industrial data of the client using an orthogonality constraint loss; uploading the trained parameter of the shared layer of each client to the server for averaging and aggregation; sending the aggregated parameter to each client; updating the parameter of the shared layer of each client based on the aggregated parameter; repeating the training and parameter updating process of the shared layer until a count of iterations equals to a count of policy switching communications; training the shared layer and the personalized layer to obtain a trained local model of the client.

CPC Classifications