Research on Personalized Federated Learning Algorithm in Industrial Internet of Things
In order to build a joint model without directly sharing the original data,federated learning came into being.However,in the complex industrial IoT environment,the application of federated learning faces two major challenges:1)Industrial IoT devices are het-erogeneous with each other,and the existence of outdated and offline devices greatly slows down the training speed of federated learn-ing;2)The data owned by different data owners are heterogeneous with each other,and the local models of the clients are quite differ-ent.Simply averaging the local models cannot obtain a high-quality model suitable for all clients.In order to solve the above challen-ges,this paper designs a federated learning architecture that integrates digital twins to achieve efficient scheduling of device resources.In addition,this paper proposes a personalized federated learning algorithm based on parameter decoupling and clustering,which can not only meet the individual needs of users,but also realize the deep collaboration of isomorphic clients.Experimental results verify the effectiveness of the proposed personalized federated learning algorithm.
federated learningindustrial internet of thingsdigital twinpersonalization