Wireless federated learning framework and resource optimization method

Abstract

A wireless federated learning (FL) framework and a resource optimization method are provided to resolve a problem that FL is not suitable for many hardware-constrained Internet of Things (IoT) devices with a small amount of computing resources. In the framework, users with sufficient computing resources upload locally trained model parameters to a base station, and users with limited computing resources only need to send training data to the base station. The base station performs data training and model aggregation to obtain a global model. In this way, the users with limited computing resources and the users with sufficient computing resources cooperatively train the global model. To improve a data transmission rate and reduce an aggregation error of FL, a non-convex optimization problem is constructed to jointly design user transmit power and a reception strategy of the base station, and solves the problem through a successive convex approximation (SCA) method.