| Autor: |
Deepak Gupta, Joel J. P. C. Rodrigues, Tariq Hussain Sheikh, Bhrigu Kansra, Harshita Diddee, Ashish Khanna |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Advances in Intelligent Systems and Computing ISBN: 9789811625961 |
| DOI: |
10.1007/978-981-16-2597-8_21 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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