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

by   Chen Zhao, et al.

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.



page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8


Hierarchical Quantized Federated Learning: Convergence Analysis and System Design

Federated learning is a collaborative machine learning framework to trai...

Personalized Federated Learning: An Attentive Collaboration Approach

For the challenging computational environment of IOT/edge computing, per...

Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation

Federated learning obtains a central model on the server by aggregating ...

Optimising Communication Overhead in Federated Learning Using NSGA-II

Federated learning is a training paradigm according to which a server-ba...

E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI

Traditionally, AI models are trained on the central cloud with data coll...

Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification

Person re-identification (ReID) aims to re-identify a person from non-ov...

Yggdrasil: Privacy-aware Dual Deduplication in Multi Client Settings

This paper proposes Yggdrasil, a protocol for privacy-aware dual data de...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.