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Federated Learning for Intelligent Transportation Systems, Computer Science – PhD (Funded) Ref: 4506


   College of Engineering, Mathematics and Physical Sciences

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  Dr Johan Wahlström, Dr Man Luo  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project

Thanks to recent advances in microchip technology and AI algorithms, intelligence is moving from cloud-based architectures to decentralised entities, such as vehicles and smartphones. This transformation has given rise to the term federated learning, which refers to techniques for training machine learning algorithms in a collaborative and distributed manner. Federated learning is, among other things, expected to lead to significant developments in the transportation sector, developments which will be accelerated by the introduction of 5G networks, new developments in mobile edge computing, and an increasing number of sensing modalities. However, to fully realise the potential of federated learning in intelligent transportation systems, there are several obstacles that need to be overcome, including data privacy, scalability, and the lack of annotated data. 

This project will focus on how federated learning can be integrated into intelligent transportation systems, both as a way of improving upon existing transportation solutions, but also to develop novel applications enabled by the emergence of federated learning. The considered inference tasks may include high-level traffic analysis, demand prediction for vehicle sharing systems, transportation mode classification, and analysis of driving behaviour.


Funding Notes

The University of Exeter’s Department of Computer Science is inviting applications for a fully-funded PhD studentship to commence on 9 January 2023 or as soon as possible thereafter. For eligible students the studentship will cover Home tuition fees plus an annual tax-free stipend of at least £16,062 for 3.5 years full-time, or pro rata for part-time study. The student would be based in the Department of Computer Science in the Faculty of Environment, Science and Economy.
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