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Bayesian federated learning for collaborative intelligence in modern smart car-road infrastructure networks

  • Full or part time
  • Application Deadline
    Monday, February 10, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Machine learning techniques often require training data to be collected and stored in a centralised HPC/data centre, which can be inefficient in modern Internet of Things enabled scenarios such as Internet of Vehicle or Connected Cars. Federated Learning [1] is an emerging technique that aims to support remote/mobile devices to collaboratively learn a cloud-shared model while keeping all the private data locally.

This PhD project aims to enhance inference and reasoning abilities on both terminal (vehicle level) and central cloud through collaborative intelligence [2], this may involve research on intelligence computation partitioning schemes, Bayesian collaborative decision making, and multiple objective optimization. The outcome of the project may include novel and efficient federated learning assisted collaborative intelligence algorithms for next generation of Internet of Vehicle and corresponding toolbox or software.

The project will contribute to the exploration of safe and trustworthy AI research, and the improvement of the reliability and efficiency of smart cities, autonomous vehicles, automated industrial assembly, etc.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its second cohort of at least 10 students to start in September 2020. Further details can be found at: http://www.bath.ac.uk/centres-for-doctoral-training/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Applicants should hold, or expect to receive, a First or Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

Informal enquiries about the research should be directed to Dr Xi Chen: .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP02&code2=0002

Start date: 28 September 2020

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum in 2019/20, increased annually in line with the GDP deflator) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

How good is research at University of Bath in Computer Science and Informatics?

FTE Category A staff submitted: 24.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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