Abstract
In Intelligent Transport Systems (ITS), the collection of diverse data is a major practical roadblock; not only can their data be personally identifiable, i.e. private, but also the lack of incentive for entities to participate in any kind of collaborative training is also severely limited due to the added computational expense of training collaborative models locally. In this paper, we propose BlockFITS: A Vehicle-to-BlockChain-to-Vehicle (V2B2V) federated learning enabled model training paradigm for ITS entities. In addition to which we propose a data augmentation scheme that operates with cooperative training to generate an incentive for entity participation. The immutability and decentralised features of the Blockchain system leverage the federated-like averaging of synthetically generated data samples that generate incentives for the participation of entities in such a training setup. BlockFITS can be practically deployed in future ITS systems to improve the autonomous driving system, pedestrian safety, and vehicular object detection or more due to its model-constraint-free characteristics which provide access to a synthetic and global data whilst maintaining data privacy.
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Acknowledgements
We are very grateful for the partial funding provided by FCT/MCTES through national funds and when applicable co-funded EU funds under the Project UIDB/50008/2020, and by Brazilian National Council for Scientific and Technological Development—CNPq, via Grant No. 309335/2017-5. This work would not have been possible without this support.
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Kansra, B., Diddee, H., Sheikh, T.H., Khanna, A., Gupta, D., Rodrigues, J.J.P.C. (2022). BlockFITS: A Federated Data Augmentation Modelling for Blockchain-Based IoVT Systems. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1388. Springer, Singapore. https://doi.org/10.1007/978-981-16-2597-8_21
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