基于泛化信息的公关识别和分析无线联合学习

Jianxin Liu, Ying Li, Jian Zhou, Huangsheng Hua, Pu Zhang
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引用次数: 0

摘要

本文介绍了一种在无线通信场景中利用联合学习(FL)、利用广义信息进行个人风险(PR)识别的新方法。主要重点是利用各种无线设备上分布式数据的力量,同时确保数据隐私和安全,这是个人风险评估中的一个关键问题。为此,我们提出了一种基于 FL 的模型,该模型能有效聚合从各种分散数据源中学习到的信息,从而分析公关因素。所提出的方法包括在单个设备上训练局部模型,然后将这些模型聚合起来,形成一个全面的全局模型。这一过程不仅通过在设备上保留敏感信息来保护数据隐私,还利用无线设备的广泛可用性和连接性来提高数据的丰富性和模型的稳健性。为了应对无线环境带来的挑战,如数据异构性和通信限制,我们进一步实施了先进的聚合算法和优化技术,以适应这些独特的条件。最后,我们根据联合学习过程的识别准确率和收敛率这两个主要指标来评估所提出方法的性能。通过大量的模拟和实际实验,我们证明了我们的方法不仅能实现高精度的 PR 识别,还能确保快速收敛,使其成为无线网络实时风险评估的可行解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wireless federated learning for PR identification and analysis based on generalized information

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.

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