FLAV:为保护自主车辆隐私而进行的联合学习

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yingchun Cui , Jinghua Zhu , Jinbao Li
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引用次数: 0

摘要

自动驾驶汽车系统致力于在未来的道路上提供更安全、更高效、更便捷的交通。然而,对车辆数据隐私和安全的担忧依然存在。联合学习作为一种分散的机器学习方法,允许多个设备或数据源在不共享原始数据的情况下协作训练模型,从而提供必要的隐私保护。在本文中,我们为自动驾驶汽车提出了一个隐私保护框架,命名为 FLAV。首先,我们采用多链并行聚合策略来传输模型参数,并设计了模型参数过滤机制,通过过滤掉某些车辆的本地模型参数来减少通信开销,从而减轻带宽压力。其次,我们引入了一种动态调整机制,通过比较每辆车的本地参数和链中前一辆车的累积参数,自动调整正则化强度。这种机制兼顾了局部训练和全局一致性,确保了模型对局部数据的适应性,同时改善了链中车辆之间的协调性。实验结果表明,我们提出的方法降低了通信成本,同时提高了模型的准确性和隐私保护水平,有效确保了自动驾驶数据的安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FLAV: Federated Learning for Autonomous Vehicle privacy protection
Autonomous Vehicle Systems are committed to safer, more efficient and more convenient transportation on the roads of the future. However, concerns about vehicle data privacy and security remain significant. Federated Learning, as a decentralized machine learning approach, allows multiple devices or data sources to collaboratively train models without sharing raw data, providing essential privacy protection. In this paper, we propose a privacy-preserving framework for autonomous vehicles, named FLAV. First, we use a multi-chain parallel aggregation strategy to transmit model parameters and design a model parameter filtering mechanism, which reduces communication overhead by filtering out the local model parameters of certain vehicles, thereby alleviating bandwidth pressure. Second, we introduce a dynamic adjustment mechanism that automatically adjusts regularization strength by comparing each vehicle’s local parameters with the cumulative parameters of preceding vehicles in the chain. This mechanism balances local training with global consistency, ensuring the model’s adaptability to local data while improving coordination between vehicles in the chain. Experimental results demonstrate that our proposed method reduces communication costs while improving model accuracy and privacy protection level, effectively ensuring the security of autonomous driving data.
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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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