Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Abstract

This work was supported in part by ROIS NII Open Collaborative Research 2020-20S0502 and in part by JSPS KAKENHI under Grants 18KK0279 and 19H04093, Japan.

Blockchain for Vehicular Internet of Things: Recent Advances and Open Issues

“…Due to the limited communication and computing resources at each vehicle, future vehicular IoT can consider collaboration between multiple entities to achieve a higher computational capability and lower latency. In recent years, federated learning [ 5 ] has attracted great interest in utilizing the knowledge from multiple devices to improve the intelligence of a system. The conventional federated learning relies on a centralized server to aggregate feedback from different clients.…”

“…Federated learning [ 5 , 6 ], also known as collaborative learning, is a distributed learning technology that enables knowledge sharing between different vehicles with privacy protection. In federated learning, each vehicle (client) trains a local model based on the sensor data it perceives and uploads the trained model to the central server.…”

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

“…Moreover, more research is needed to improve the performance of FL frameworks as it was shown in [39], [43], [140] that although the frameworks are effective at reducing latency, they may be less effective at guaranteeing the required data privacy. Several issues regarding FL techniques were discussed in the recent work of [37], [39], [40], [90], [138], [142], [144], [146], [147], [152]- [154]. The work in [154] discussed the technical challenges of applying FL in vehicular IoT, and highlighted the necessary improvements that should be made for IoT technologies.…”

Intelligent Signal Classification in Industrial Distributed Wireless Sensor Networks Based Industrial Internet of Things

“…With federated learning, the privacy of users' personal data can be protected, and the signal classification could be improved by deep learning technologies. In view of these tremendous benefits, federated learning has been deemed as a promising technology in IDWSN signal modulation classification, vehicular Internet of Things and other fields [20].…”