Privacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection

نویسندگان

چکیده

The drowsiness affects the driver’s sensory, cognitive, and psychomotor abilities, which are necessary for safe driving. Drowsiness detection is a critical technique to avoid traffic accidents. federated learning (FL) can solve problem of insufficient driver facial data by utilizing different industrial entities’ data. However, in FL system, privacy information drivers might be leaked. In addition, reducing communication costs maintaining model performance also challenge scenarios. this work, we propose transfer method with privacy-preserving protocol detection, named PFTL-DDD. We use fine-tuning on initial system. Furthermore, CKKS-based applied preserve drivers’ encrypting exchanged parameters. experimental results show that PFTL-DDD superior terms accuracy efficiency as compared conventional NTHU-DDD dataset YAWDD dataset. theoretical analysis demonstrates proposed reduce cost security protect personal privacy.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2022

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2022.3192454