Details
| Original language | English |
|---|---|
| Title of host publication | 18th European Conference on Antennas and Propagation |
| Subtitle of host publication | EuCAP 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 5 |
| ISBN (electronic) | 9788831299091 |
| ISBN (print) | 979-8-3503-9443-6 |
| Publication status | Published - 2024 |
| Event | 2024 18th European Conference on Antennas and Propagation (EuCAP) - Glasgow, United Kingdom (UK) Duration: 17 Mar 2024 → 22 Mar 2024 https://www.eucap2024.org/ |
Abstract
This study explores the use of Federated Learning (FL) in classifying ISAR images for autonomous driving. Automotive radar systems, operating at millimeter-wave frequencies, offer critical safety features. ISAR images are powerful for target recognition but pose challenges in real-world scenarios. FL, a decentralized training approach, is employed for data privacy while maintaining competitive accuracy. Our findings reveal that FL achieves commendable performance compared to centralized models, ensuring data confidentiality by keeping the information on local devices and centrally sharing only the model weights. In conclusion, this research demonstrates FL's potential in improving ISAR-based target classification for autonomous driving, making it suitable for privacy-sensitive applications.
Keywords
- Automotive, Electromagnetics, Federated Learning, ISAR
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Mathematics(all)
- Modelling and Simulation
- Physics and Astronomy(all)
- Instrumentation
- Physics and Astronomy(all)
- Radiation
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
18th European Conference on Antennas and Propagation: EuCAP 2024. Institute of Electrical and Electronics Engineers Inc., 2024.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Target Classification through ISAR for Autonomous Vehicles based on Federated Learning
AU - Violi, Vincenzo
AU - Usai, Pierpaolo
AU - Brizi, Danilo
AU - Singh, Gurtaj
AU - Fisichella, Marco
AU - Isernia, Tommaso
AU - Monorchio, Agostino
N1 - Publisher Copyright: © 2024 18th European Conference on Antennas and Propagation, EuCAP 2024. All Rights Reserved.
PY - 2024
Y1 - 2024
N2 - This study explores the use of Federated Learning (FL) in classifying ISAR images for autonomous driving. Automotive radar systems, operating at millimeter-wave frequencies, offer critical safety features. ISAR images are powerful for target recognition but pose challenges in real-world scenarios. FL, a decentralized training approach, is employed for data privacy while maintaining competitive accuracy. Our findings reveal that FL achieves commendable performance compared to centralized models, ensuring data confidentiality by keeping the information on local devices and centrally sharing only the model weights. In conclusion, this research demonstrates FL's potential in improving ISAR-based target classification for autonomous driving, making it suitable for privacy-sensitive applications.
AB - This study explores the use of Federated Learning (FL) in classifying ISAR images for autonomous driving. Automotive radar systems, operating at millimeter-wave frequencies, offer critical safety features. ISAR images are powerful for target recognition but pose challenges in real-world scenarios. FL, a decentralized training approach, is employed for data privacy while maintaining competitive accuracy. Our findings reveal that FL achieves commendable performance compared to centralized models, ensuring data confidentiality by keeping the information on local devices and centrally sharing only the model weights. In conclusion, this research demonstrates FL's potential in improving ISAR-based target classification for autonomous driving, making it suitable for privacy-sensitive applications.
KW - Automotive
KW - Electromagnetics
KW - Federated Learning
KW - ISAR
UR - http://www.scopus.com/inward/record.url?scp=85192446737&partnerID=8YFLogxK
U2 - 10.23919/EuCAP60739.2024.10501261
DO - 10.23919/EuCAP60739.2024.10501261
M3 - Conference contribution
AN - SCOPUS:85192446737
SN - 979-8-3503-9443-6
BT - 18th European Conference on Antennas and Propagation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 18th European Conference on Antennas and Propagation (EuCAP)
Y2 - 17 March 2024 through 22 March 2024
ER -