About the Project
Overview
Unmanned Aerial Vehicle (UAV) swarms are becoming increasingly important for defence, emergency response, infrastructure inspection, and future autonomous systems. These swarms can use federated learning (FL), where multiple UAVs collaboratively train artificial intelligence models without sharing raw mission data. This supports privacy and efficiency, but it also creates a serious security risk: a compromised UAV may send poisoned model updates that corrupt the shared intelligence of the swarm.
This PhD will develop lightweight, trust-aware methods to make FL models safer for UAV swarms. The successful candidate will design algorithms that identify suspicious model updates, reduce the influence of compromised UAVs, and preserve useful learning from honest UAVs operating with different data and unreliable communications.
This is a part-time PhD studentship, potentially starting in January 2027, with a 60/40 research-teaching split, offering advanced research training and valuable university teaching experience. The project includes training in AI, cyber security, FL, privacy-preserving computation, edge intelligence, and UAV simulation. The candidate will join MMU’s supportive doctoral community, with collaboration opportunities linked to the National Edge AI Hub through Newcastle University.
The successful candidate may also have the opportunity to engage with a spin-out activity in secure distributed AI, subject to relevant approvals.
Objectives
This project aims to develop lightweight, trust-aware private federated learning (PFL) for secure UAV swarms under model poisoning attacks. The objectives include:
- Define a UAV-PFL threat model covering poisoning, backdoor attacks, non-IID data, and intermittent communication.
- Build a reproducible UAV-PFL benchmark for evaluating state-of-the-art secure and robust aggregation methods.
- Design multi-evidence trust metrics using update behaviour, validation impact, temporal consistency, and communication reliability.
- Develop a privacy-aware trust-calibrated aggregation algorithm to down-weight suspicious updates.
- Evaluate accuracy, attack resilience, privacy leakage, communication overhead, computation cost, and deployment feasibility.
Candidate requirements
Applicants should hold, or expect to obtain, an undergraduate and preferably a postgraduate qualification in computer science, artificial intelligence, cyber security, data science, or a closely related discipline.
Essential
- Good programming skills, preferably in Python/C#.
- Experience with machine learning, deep learning, or experimental AI evaluation.
- Interest in secure distributed AI, federated learning, adversarial machine learning, UAV systems, or edge intelligence.
- Strong analytical, problem-solving, and independent research skills.
- Good written communication skills for producing research papers and thesis chapters.
Desirable
- Federated learning, privacy-preserving computation, network security, or model poisoning attacks.
- Simulation tools, deep learning frameworks, or open-source research code.
Funding
These are doctoral teaching assistant positions that combine a PhD programme with a university teaching contract. Your time will be split approximately 60% on research and 40% on teaching. This provides excellent preparation for candidates considering an academic career at a university. The teaching component will typically run over the 22 teaching weeks per year and the 4 assessment weeks. You will help deliver an outstanding student experience by supporting lead academics with classroom and lab teaching and assessment, further building the skills developed within your PhD research programme.
The position is grade 6 with a current salary of £31,236 and includes payment of home PhD tuition fees for the duration of the 6-year award. Home students can apply. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role.
How to apply
The School of Computing and Mathematics is inviting applications for eight independent doctoral projects, with five funded Doctoral Teaching Assistant positions available. All applications will go through an initial review stage conducted by the project supervisors. Candidates selected from this stage will be invited to an interview, where final funding decisions will be made based on performance across all projects.
If you have any questions, contact the principal supervisor, Dr Muhammad Atif Ur Rehman.
To apply you will need to complete the online application form for a part time PhD in Computing & Digital Technology.
Please complete the Doctoral Project Applicant Form, and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.
Please upload these documents in the supporting documents section of the University’s Admissions Portal or send them to the PGR Admissions team at pgradmissions@mmu.ac.uk.
Please quote the reference: SciEng-DTA Jan 2027-MR-AI Security UAV
Application link: PhD Computing and Digital Technology — six years part-time
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