Deakin-Coventry Cotutelle - Federated learning for safe/secure connected and autonomous vehicles

This is a doctoral Cotutelle project in ‘federated learning for safe/secure connected and autonomous vehicles' between Deakin University (Australia) and Coventry University (United Kingdom).' The project is led by Coventry University

Deakin Project Supervisor

Additional Supervision


Deakin Geelong Campus (Australia) and  Coventry University (United Kingdom)

Research topic

This is a doctoral cotutelle project between Deakin University (Australia) and Coventry University (United Kingdom).

The successful PhD Student will be awarded a scholarship from Coventry University with the supervision team being drawn from Deakin University and Coventry University. The PhD Student will graduate with two testamurs, one from Deakin University and one from Coventry University, each of which recognises that the program was carried out as part of a jointly supervised doctoral program. The program is for a duration of 3.5 years and scheduled to commence in September 2024.

The PhD Student is anticipated to spend at least 6 months of the total period of the program at Deakin University, with the remainder of the program based at Coventry University.

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 connected and autonomous vehicles (CAVs) due to their unique constraints (e.g., high mobility and stringent safety/security requirements). Overcoming these issues has the potential to significantly improve the performance, safety, and cyber resilience of CAVs.

Project aim

This proposal aims at constructing a hierarchical federated learning for safe and secure connected and autonomous vehicles (CAVs). The constructed framework will combine elements of Artificial intelligence (AI) and machine learning (ML)  (e.g. FL aggregation mechanisms), connectivity (e.g. 5G/6G optimised scheduling and resource allocation) and computation (e.g. device/fog/edge/cloud computing) to support significant use cases in the automotive industry.

Important dates

Applications close 5pm, Monday 29 April 2024


This scholarship is supported by Coventry University, is available over 3.5 years and includes:

  • Stipend of £18,622 per annum
  • A Tuition Fee Waiver
  • Travel Support Package including one return economy airfare to Deakin University to support residency period in Australia
  • Student visa and health insurance costs for period of residency at Deakin University in Australia

Deakin University will also provide a full tuition fee waiver for a duration of up to 4 years.

Eligibility criteria

To be eligible you must:.

  • meet the PhD entry requirements of both Deakin University and Coventry university, including English language proficiency requirements
  • be enrolling full time
  • be able to physically locate to both Coventry University (UK) and Deakin University (Australia)

Please refer to the research degree entry pathways page and Coventry’s research entry criteria page for further information.

How to apply

Applicants should firstly contact Prof. Jemal Abawajy to discuss the project. After discussing your application with the Deakin Supervisor, you will be required to submit an Expression of interest directly with the relevant Faculty.
If successful, you will be invited by Deakin University to lodge a formal HDR application.

The successful applicant will also be required to lodge a separate PhD application to Coventry University via the Coventry University application page.

Please be aware that screening for this advert will commence immediately and the scholarship may be awarded prior to the closing date.

Contact us

For more information about this scholarship, please contact:

Prof. Jemal Abawajy
+613 5227 1376
Visit Jemal's LinkedIn profile