利用生物信号进行隐私保护分层联合学习,以检测驾驶时的嗜睡状态

Sergio López Bernal, José Manuel Hidalgo Rogel, Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán
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引用次数: 0

摘要

为应对全球对驾驶过程中嗜睡问题的安全关切,欧盟规定新车必须集成符合一般数据保护法规的检测系统。为了在识别嗜睡模式的同时保护驾驶员的数据隐私,最近有文献将联合学习(FL)与不同的生物信号(如面部表情、心率、脑电图(EEG)或脑电图(EOG))相结合。然而,现有的解决方案并不适合嗜睡检测,因为在嗜睡检测中,不同的利益相关者希望在不同层面进行协作,同时保证数据的隐私性。目前,还没有作品对使用分层动态脑电图(HFL)与脑电图和眼电图生物信号的好处进行评估,也没有作品对 HFL 与传统动态脑电图和机器学习(ML)方法进行比较,以在确保数据保密性的同时检测车轮上的瞌睡情况。因此,本研究提出了一个灵活的框架,通过使用 HFL、FL 和 ML 对 EEG 和 EOG 数据进行嗜睡识别。为了验证该框架,本研究定义了三个运输公司的场景,目的是在不影响数据保密性的情况下共享司机的数据,并定义了一个两级分层结构。本研究提出了三个增量用例(UC)来评估检测性能:UC1) 公司内部 FL,准确率为 77.3%,同时确保了司机个人数据的隐私;UC2) 公司间 FL,已知司机准确率为 71.7%,新对象准确率为 67.1%,确保了公司之间的数据保密性,但未确保组织内部的数据保密性;UC3) 公司间 HFL,确保了公司内部和公司之间的全面数据隐私,培训对象准确率为 71.9%,新对象准确率为 65.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

In response to the global safety concern of drowsiness during driving, the European Union enforces that new vehicles must integrate detection systems compliant with the general data protection regulation. To identify drowsiness patterns while preserving drivers’ data privacy, recent literature has combined Federated Learning (FL) with different biosignals, such as facial expressions, heart rate, electroencephalography (EEG), or electrooculography (EOG). However, existing solutions are unsuitable for drowsiness detection where heterogeneous stakeholders want to collaborate at different levels while guaranteeing data privacy. There is a lack of works evaluating the benefits of using Hierarchical FL (HFL) with EEG and EOG biosignals, and comparing HFL over traditional FL and Machine Learning (ML) approaches to detect drowsiness at the wheel while ensuring data confidentiality. Thus, this work proposes a flexible framework for drowsiness identification by using HFL, FL, and ML over EEG and EOG data. To validate the framework, this work defines a scenario of three transportation companies aiming to share data from their drivers without compromising their confidentiality, defining a two-level hierarchical structure. This study presents three incremental Use Cases (UCs) to assess detection performance: UC1) intra-company FL, yielding a 77.3% accuracy while ensuring the privacy of individual drivers’ data; UC2) inter-company FL, achieving 71.7% accuracy for known drivers and 67.1% for new subjects, ensuring data confidentiality between companies but not intra-organization; and UC3) HFL inter-company, which ensured comprehensive data privacy both within and between companies, with an accuracy of 71.9% for training subjects and 65.5% for new subjects.

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