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Federated learning concept which is a technique where machine learning models are trained on data that is distributed across multiple devices

Federated Learning Techniques for Cross-Domain Wi-Fi Sensing in Healthcare

Eligibility: UK/International (including EU) graduates with the required entry requirements

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 25 October 2024

Interview date: Will be confirmed to shortlisted candidates

Start date: January 2025

For further details contact: Dr Syed Aziz Shah


Introduction

This is an exciting opportunity to study a PhD as part of a cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. The PhD Student will graduate with two PhDs, 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 (funding only for 3.5 years, maximum allowed time 4 years) and scheduled to commence in January 2025. 

Project details

Federated learning (FL) is a machine learning technique that allows multiple devices to collaboratively train a global model while keeping their data local. This is particularly useful for cross-domain Wi-Fi sensing, where devices in different environments collect different types of data.

One of the key challenges in cross-domain Wi-Fi sensing is the heterogeneity of data. Data collected from different environments may have different distributions due to factors such as the number of devices, the size and shape of the environment, and the presence of obstacles. This can make it difficult to train a single model that can accurately perform Wi-Fi sensing tasks across all domains.
This PhD project will address this research challenge by allowing devices to train local models on their own data. The local models will then be aggregated to train a global model.
The potential PhD student working on federated learning for cross-domain Wi-Fi sensing is expected to work on a variety of challenging and interesting problems. Some specific research areas that could be explored include:

  • Developing new FL techniques that are more efficient and effective for cross-domain Wi-Fi sensing.
  • Designing FL-based systems for specific cross-domain Wi-Fi sensing applications, such as HAR, indoor localization, and smart home monitoring.
  • Evaluating the performance of FL-based cross-domain Wi-Fi sensing systems in real-world settings.
  • The PhD student will also have the opportunity to collaborate with other researchers in the field and to publish their work in top academic conferences and journals.

Funding

Tuition fees and bursary

Benefits

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. 

Candidate specification

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 Bachelor's degree in a relevant field requiring at least four years of full-time study, and which normally includes a research component which is equivalent to at least 25% of a year’s full-time study in the fourth year, with achievement of a grade for the project equivalent to a H1 standard or 80%

 OR

  •  A Masters degree, with a significant research component, in a relevant subject area, with overall mark at minimum Distinction.
    • In addition, the mark for the Masters thesis (or equivalent) must be a minimum of 80%.
  • Please note that where a candidate has 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 to complete the PhD within a prescribed period of study

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)

How to apply

To find out more about the project, please contact Dr Syed Aziz Shah

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

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