公开(公告)号：US20240297700A1

公开(公告)日：2024-09-05

申请号：US18387068

申请日：2023-11-06

Applicant: Beijing University of Posts and Telecommunications

Inventor： Hui TIAN ,  Wanli NI ,  Ping ZHANG ,  Shaoshuai FAN ,  Gaofeng NIE ,  Shilin TAO

IPC: H04B7/06

CPC classification number: H04B7/0626 ,  H04B7/0639

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.