Federated Learning in Vehicular Internet of Things
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 485
Special Issue Editors
Interests: network virtualization; cloud-native networking; edge-cloud computing; federated-split learning; Internet of Things; Internet of Intelligence
Special Issue Information
Dear Colleagues,
Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems, comprise a large number of devices that may generate and process a huge amount of privacy-sensitive data and require efficient utilization of the communication, computation, and storage resources. Therefore, vehicular IoT calls for effective and efficient machine learning (ML) mechanisms that can involve a large number of IoT devices in collaborative model training while protecting the data privacy of the participating devices.
Federated learning (FL) is an emerging ML paradigm in which a group of user devices (clients), each having its local dataset, collaborate in training a global model under the coordination of a controller (server). FL keeps the raw training data on user devices and only shares the locally trained models between clients and server, thus enhancing data privacy and improving communication efficiency. Therefore, FL offers a promising ML approach that may be applied to various vehicular IoT systems. On the other hand, deploying FL in vehicular IoT faces some technical issues that must be fully addressed. For example, the large number of IoT devices with diverse system capabilities and heterogeneous data distributions, together with the highly dynamic networking environment, make federated learning in vehicular IoT particularly challenging.
This Special Issue aims to present the latest progress in the research and technological development of federated learning in vehicular IoT systems. The Special Issue covers, but is not limited to, the following topics:
- Federated learning algorithms for vehicular IoT;
- Federated learning frameworks for vehicular IoT;
- Resource-efficient federated learning in vehicular IoT;
- Privacy and security protection for federated learning in vehicular IoT;
- Applying federated learning to enhance vehicular IoT performance;
- Federated-learning-based vehicular IoT applications.
Dr. Qiang Duan
Dr. Jun Huang
Guest Editors
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Keywords
- federated learning
- machine learning
- Internet of Things (IoT)
- vehicular networks
- edge/fog computing
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