IEEE Consumer Electronics Magazine - November/December 2022 - 65

only vehicles yet a diversity of IoVT devices, the
training procedure is advised to be offloaded to the
VEC nodes to alleviate the computing burden from
vehicular deviceswith limited resources.15
CONCLUSION
This study presents an informative discussion
concerning the role of FL in developing a reliable
and secure IoVT system. In particular, we debated
and contrast various taxonomies of FL solutions,
practical aspects, possible application in IoVT.
Then, the method of protecting the privacy of FL is
explained and from different perspectives along
with their merits and demerits. Then, a Fed-GCRN
framework is introduced to modeling spatial-temporal
relations in traffic flows, which are necessary
for TFF. An ADP is designed to maximize privacy
without affecting the performance Fed-GCRN.
Finally, current challenges facing the FL in designing
IoVT systems, also figure out the promising solutions
to be investigated in future research. This
study offers a concise discussion for accelerating
the research efforts in secure and intelligent IoVT
applications.
&REFERENCES
1. Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang,
" Federated learning for data privacy preservation in
vehicular cyber-physical systems, " IEEENetw.,
vol. 34, no. 3, pp. 50-56, May/Jun. 2020.
2. G. Muhammad and M. Alhussein, " Security, trust, and
privacy for the internet of vehicles: A deep learning
approach, " IEEEConsum. Electron. Mag.,tobe
published, doi: 10.1109/MCE.2021.3089880.
3. P. Paikrao, A. Mukherjee, D. K. Jain, P. Chatterjee, and
W. Alnumay, " Smart emotion recognition framework: A
secured IOVT perspective, " IEEEConsum.Electron.
Mag., to bepublished, doi: 10.1109/MCE.2021.3062802.
4. A. Hammoud, H. Sami, A. Mourad, H. Otrok,
R. Mizouni, and J. Bentahar, " AI, blockchain, and
vehicular edge computing for smart and secure iov:
Challenges and directions, " IEEEInternet Things Mag,
vol. 3, no. 2, pp. 68-73, Jun. 2020.
5. J. Posner, L. Tseng, M. Aloqaily, and Y. Jararweh,
" Federated learning in vehicular networks:
Opportunities and solutions, " IEEENetw., vol. 35,
no. 2, pp. 152-159, Mar./Apr. 2021.
6. Y. Liu, J. J. Q. Yu, J. Kang, D. Niyato, and S. Zhang,
" Privacy-Preserving traffic flow prediction: A federated
learning approach, " IEEEInternet Things J., vol. 7,
no. 8, pp. 7751-7763, Aug. 2020.
7. A. Makkar, U. Ghosh, D. B. Rawat, and J. Abawajy,
" FedLearnSP: Preserving privacy and security using
federated learning and edge computing, " IEEE
Consum. Electron. Mag., to be published,
doi: 10.1109/MCE.2020.3048926.
8. S. Abdulrahman, H. Tout, H. Ould-Slimane,
A. Mourad, C. Talhi, and M. Guizani, " A survey on
federated learning: The journey from centralized to
distributed on-site learning and beyond, " IEEE
Internet Things J., vol. 8, no. 7, pp. 5476-5497,
Apr. 2021.
9. Z. Li et al., " RR-LADP: A privacy-enhanced federated
learning scheme for internet of everything, " IEEE
Consum. Electron. Mag., vol. 10, no. 5, pp. 93-101,
Sep. 2021.
10. Z. Li, V. Sharma, and S. P. Mohanty, " Preserving data
privacy via federated learning: Challenges and
solutions, " IEEEConsum. Electron. Mag., vol. 9, no. 3,
pp. 8-16, May 2020.
11. M. Hasan, S. Mohan, T. Shimizu, and H. Lu, " Securing
Vehicle-to-Everything (V2X) communication
platforms, " IEEETrans. Intell. Veh., vol. 5, no. 4,
pp. 693-713, Dec. 2020.
12. O. A. Wahab, A. Mourad, H. Otrok, and T. Taleb,
" Federated machine learning: Survey, multi-level
classification, desirable criteria and future directions in
communication and networking systems, " IEEE
Commun. Surv. Tut., vol. 23, no. 2, pp. 1342-1397,
Apr.-Jun. 2021.
13. W. Y. B. Lim et al., " Federated learning in mobile edge
networks: A comprehensive survey, " IEEECommun.
Surv. Tut., vol. 22, no. 3, pp. 2031-2063, Jul.-Sep.
2020.
14. S. K. Singh, P. K. Sharma, Y. Pan, and J. H. Park,
" BIIoVT: Blockchain-based secure storage
architecture for intelligent internet of vehicular
things, " IEEE Consum. Electron. Mag.,tobe
published, doi: 10.1109/MCE.2021.3089992.
15. D. C. Nguyen et al., " Federated learning meets
blockchain in edge computing: Opportunities and
challenges, " IEEEInternet Things J, vol. 8, no. 16,
pp. 12806-12825, Aug. 2021.
16. L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, " Adaptive
graph convolutional recurrent network for traffic
forecasting, " 2020, arXiv:2007.02842.
November/December 2022
65
http://dx.doi.org/10.1109/MCE.2020.3048926 http://dx.doi.org/10.1109/MCE.2021.3089880 http://dx.doi.org/10.1109/MCE.2021.3062802 http://dx.doi.org/10.1109/MCE.2021.3089992
IEEE Consumer Electronics Magazine - November/December 2022

Table of Contents for the Digital Edition of IEEE Consumer Electronics Magazine - November/December 2022
