Hierarchical federated learning framework for safe and secure connected and autonomous vehicles
Eligibility: UK/International (including EU) graduates with the required entry requirements
Funding details: Bursary, tuition fees, and stipend
Duration: Full-time – between three and three and a half years fixed term
Application deadline: October 25 2023
Interview dates: Will be confirmed to shortlisted candidates
Start date: January 2024
To find out more about the project, please contact Dr. Faouzi Bouali
This PhD project is part of the Cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia
The successful applicant will spend the 1st year at Coventry University and the following year at Deakin University and then the final 1.5 years at Coventry University.
The supervision team will be drawn from the two Universities.
Fuelled by the availability of more data and computing power, recent breakthroughs in cloud-based artificial intelligence (AI)/machine learning (ML) have transformed various aspects of our lives, including face recognition and medical diagnosis. However, this centralised approach is not suitable for connected and autonomous vehicles (CAVs) due to the reduced capacity of wireless links, long delays in exchanging data with the cloud, limited scalability, and data privacy concerns. To overcome these limits, federated learning (FL) has recently emerged to enable many clients to collaboratively train a model without sharing their local data. While FL reduces the load on wireless links and appeases some of the privacy concerns, it is still facing several open issues related to intermittent wireless connectivity, performance, security, and scalability. These limits are exacerbated for CAVs due to their unique constraints (e.g., high mobility and stringent safety/security requirements). In this context, this proposal will build a hierarchical federated learning framework for CAVs. A hierarchy of tiers (e.g., vehicle, edge, and cloud) is introduced for better scalability, where the tier hosting the learning process is selected depending on the context. The latest features of 5G/6G networks will be leveraged to improve the performance, safety, and cyber resilience of CAVs.
Tution fee, stipend and bursary.
The successful candidate will receive comprehensive research training including technical, personal and professional skills.
All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.
Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme.
- Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates).
- A Masters degree in a relevant subject area, with overall mark at minimum Merit level. In addition, the mark for the Masters dissertation (or equivalent) must be a minimum of 80%. Please note that where a candidate has a 70-79% and can provide evidence of research experience to meet equivalency to the minimum first – class honours equivalent (80%+) additional evidence can be submitted and may include independently peer – reviewed publications, research – related awards or prizes and/ or professional reports.
- Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)
- The potential to engage in innovative research and complete the PhD within a prescribed period of study.
For an overview of each University’s entry requirements please visit:
Please note that it is essential that applicants confirm that they are able to physically locate to both Coventry University (UK) and Deakin University (Australia).
- Strong background and experience in designing/applying artificial intelligence (AI)/machine learning (ML) techniques. Prior experience in applying federated learning is a clear advantage.
- Excellent analytical, problem-solving, and software engineering skills, with prior experience implementing AI/ML algorithms using well-known frameworks (e.g., PyTorch and TensorFlow).
- Strong background in wireless communications and networking with experience carrying out link- and system-level simulations using publicly available tools (e.g., NS3, OMNeT++ and Matlab).
- Good knowledge of 5G/6G networks, cellular vehicle-to-everything (C-V2X) communication, cloud/fog/edge computing and cybersecurity. Prior hands-on experience in one or more of these technologies will be an added plus.
- Aspiration to achieve high-quality research contributions and publications in leading conferences and journals.
To find out more about the project please contact: Dr. Faouzi Bouali
How to apply
All applications require a covering letter and a 2000-word supporting statement is required showing how the applicant’s expertise and interests are relevant to the project.
All candidates must apply to both Universities.
For the Coventry application, please visit: https://pgrplus.coventry.ac.uk/
For the Deakin application, please visit: http://www.deakin.edu.au/research/become-a-research-student/how-to-apply-research-degrees