# FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework

arXiv: https://arxiv.org/abs/2104.12086 · HTML: https://mlsanity.com/paper/2104.12086/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation](https://mlsanity.com/paper/1903.07424/) (2019)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection](https://mlsanity.com/paper/1904.07312/) (2019)

## Abstract (arXiv)

With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. To improve the performance of the detection model, a series of techniques have been developed. However, existing work still leaves much to be desired, such as privacy disclosure and communication cost. To address these issues, we propose FedSup, a client-edge-cloud framework for privacy and efficient fatigue detection. Inspired by the federated learning technique, FedSup intelligently utilizes the collaboration between client, edge, and cloud server to realizing dynamic model optimization while protecting edge data privacy. Moreover, to reduce the unnecessary system communication overhead, we further propose a Bayesian convolutional neural network (BCNN) approximation strategy on the clients and an uncertainty weighted aggregation algorithm on the cloud to enhance the central model training efficiency. Extensive experiments demonstrate that the FedSup framework is suitable for IoV scenarios and outperforms other mainstream methods.