Wireless federated learning for PR identification and analysis based on generalized information

Abstract

This paper introduces a novel approach to personal risk (PR) identification using federated learning (FL) in wireless communication scenarios, leveraging generalized information. The primary focus is on harnessing the power of distributed data across various wireless devices while ensuring data privacy and security, a critical concern in PR assessment. To this end, we propose an FL-based model that effectively aggregates learning from diverse, decentralized data sources to analyze the PR factors. The proposed method involves training local models on individual devices, which are then aggregated to form a comprehensive global model. This process not only preserves data privacy by keeping sensitive information on the device but also utilizes the widespread availability and connectivity of wireless devices to enhance data richness and model robustness. To address the challenges posed by the wireless environment, such as data heterogeneity and communication constraints, we further implement advanced aggregation algorithms and optimization techniques tailored to these unique conditions. We finally evaluate the performance of our proposed method based on two primary metrics of identification accuracy and convergence rate of the federated learning process. Through extensive simulations and real-world experiments, we demonstrate that our approach not only achieves high accuracy in PR identification but also ensures rapid convergence, making it a viable solution for real-time risk assessment in wireless networks.

Keywords

• Federated learning
• Data privacy
• Identification accuracy
• Convergence rate