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

By Zhaoyang Du, Celimuge Wu, T. Yoshinaga, Kok-Lim Alvin Yau, Yusheng Ji, Jie Li. IEEE Open Journal of the Computer Society, 2020

333 citations recorded by the source corpus.

Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the raw data among the data owners. FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the data owners. FL attracts tremendous interests from a large number of industries due to growing privacy concerns. Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems (ITS), feature a large number of devices and privacy-sensitive data where the communication, computing, and storage resources must be efficiently utilized. FL could be a promising approach to solve these existing challenges. In this paper, we first conduct a brief survey of existing studies on FL and its use in wireless IoT. Then, we discuss the significance and technical challenges of applying FL in vehicular IoT, and point out future research directions.

Subfields: Artificial Intelligence, Computer Science Applications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6edd085285217ad85acb90e4a3e04d32c82eb3a2

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In Artificial Intelligence

In Computer Science Applications

In Electrical and Electronic Engineering

Citations in the corpus

Federated Learning in Vehicular Networks (2020, background): “Deep neural networks that are trained in an end-to-end manner on a huge amount of RADAR/LIDAR/camera sensor data, such as convolutional neural networks (CNNs), have emerged in recent years as a better alternative [3].”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, unknown): “A typical blockchain operation is described in Fig.”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “[98] Review of FL for vehicular IoT X X X X X X”
A Survey of Incentive Mechanism Design for Federated Learning (2021, unknown): “Once federated learning is proposed [6], it attracts widespread attentions from academia [7] – [11] and industry [12].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “[23] FL and vehicular networks A survey on the advances of FL and its application in vehicular networks.”
Decentralized Federated Learning: A Survey and Perspective (2023, background): “Referred to as V2V FL, this approach enables the exchange and sharing of up-to-date knowledge among vehicles and has been explored in recent studies [175], [176], [177], [178], [179], [180], [181], [182].”