Title
Real-time End-to-End Federated Learning: An Automotive Case Study
Abstract
With the development and the increasing interests in ML/DL fields, companies are eager to apply Machine Learning/Deep Learning approaches to increase service quality and customer experience. Federated Learning was implemented as an effective model training method for distributing and accelerating time-consuming model training while protecting user data privacy. However, common Federated Learning a...
Year
DOI
Venue
2021
10.1109/COMPSAC51774.2021.00070
2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)
Keywords
DocType
ISSN
Training,Adaptation models,Protocols,Wheels,Predictive models,Collaborative work,Real-time systems
Conference
0730-3157
ISBN
Citations 
PageRank 
978-1-6654-2463-9
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Hongyi Zhang101.69
Jan Bosch280788.13
Helena Holmström Olsson335737.09