Wireless federated learning framework and resource optimization method

US12381609B2 · kind B2 · utility

Assignee

• BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS · CN

Inventors

• Hui Tian · Cupertino, US
• Wanli Ni · Beijing, CN
• Ping Zhang · Beijing, CN
• Shaoshuai FAN · Beijing, CN
• Gaofeng Nie · Beijing, CN
• Shilin Tao · Beijing, CN

Key dates

• Filing date: Nov 6, 2023
• Grant date: Aug 5, 2025
• Expiry date: Jan 19, 2044

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y02D30/70
• WIPO field: Telecommunications
• WIPO sector: Electrical engineering

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.