# Privacy-Preserving Driver Drowsiness Detection with Spatial Self-Attention and Federated Learning

arXiv: https://arxiv.org/abs/2508.00287 · HTML: https://mlsanity.com/paper/2508.00287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network](https://mlsanity.com/paper/2107.09507/) (2021)
- [A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection](https://mlsanity.com/paper/1904.07312/) (2019)
- [Spatially Attentive Output Layer for Image Classification](https://mlsanity.com/paper/2004.07570/) (2020)
- [Challenges of Driver Drowsiness Prediction: The Remaining Steps to Implementation](https://mlsanity.com/paper/2109.08355/) (2021)
- [Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework](https://mlsanity.com/paper/2209.01401/) (2022)
- [A Survey on Drowsiness Detection -- Modern Applications and Methods](https://mlsanity.com/paper/2408.12990/) (2024)
- [Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services](https://mlsanity.com/paper/2106.09261/) (2021)

## Abstract (arXiv)

Driver drowsiness is one of the main causes of road accidents and is recognized as a leading contributor to traffic-related fatalities. However, detecting drowsiness accurately remains a challenging task, especially in real-world settings where facial data from different individuals is decentralized and highly diverse. In this paper, we propose a novel framework for drowsiness detection that is designed to work effectively with heterogeneous and decentralized data. Our approach develops a new Spatial Self-Attention (SSA) mechanism integrated with a Long Short-Term Memory (LSTM) network to better extract key facial features and improve detection performance. To support federated learning, we employ a Gradient Similarity Comparison (GSC) that selects the most relevant trained models from different operators before aggregation. This improves the accuracy and robustness of the global model while preserving user privacy. We also develop a customized tool that automatically processes video data by extracting frames, detecting and cropping faces, and applying data augmentation techniques such as rotation, flipping, brightness adjustment, and zooming. Experimental results show that our framework achieves a detection accuracy of 89.9% in the federated learning settings, outperforming existing methods under various deployment scenarios. The results demonstrate the effectiveness of our approach in handling real-world data variability and highlight its potential for deployment in intelligent transportation systems to enhance road safety through early and reliable drowsiness detection.