Abstract
Internet of Vehicles (IoVs) consist of connected vehicles and connected autonomous vehicles. With fog computing built within the IoV, it becomes promising for federated learning to be used in vehicular environments. One important application of such a fog computing system is distributed deep learning for decision-making tasks in autonomous driving. In this paper, a distributed training system building on top of the Named-Data Networking (NDN) architecture is introduced in order to combat the mobility challenges to the underlying network. The paper presents analyses on critical latency issues pertained to soliciting the worker CVs and collecting the partial updates. Further, the advantages of using NDN for the IoV are evaluated with comparisons to IP network through simulation. The results show promising performance gains for the evaluation cases.
The work is supported partly by the National Science Foundation under Grants No. 1719062. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Subedi, P., Yang, B., Hong, X. (2022). A Fog-Based IOV for Distributed Learning in Autonomous Vehicles. In: Calafate, C.T., Chen, X., Wu, Y. (eds) Mobile Networks and Management. MONAMI 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-94763-7_18
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