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When Federated Learning meets Mobile & IoT Healthcare: Multi-Objective Optimization Framework and Multi-modal System.

   Centre for Intelligent Healthcare

   Thursday, October 20, 2022  Competition Funded PhD Project (Students Worldwide)

About the Project

This PhD research project is part of the collaboration between CU and A*STAR. The successful candidate will have the opportunity to conduct his/her research project both at CIH in Coventry and for up to 2 years at I2R in Singapore. By engaging in research in the UK and Singapore, the programme offers researchers the opportunity to access to the state-of-art research facilities and advance their knowledge and expertise in intelligent healthcare, artificial intelligence, IoT and mobile computing, whilst developing their intercultural skills and international networks and collaborations.

The successful candidate will enrol at Coventry University, UK as their home institution and will spend up to 2 years at I2R of A*STAR. Our Coventry group is experienced in the intelligent healthcare, AI and mobile computing. The A*STAR group has rich experiences in AI, deep learning and computer vision. The complementary expertise of both groups will ensure the successful implementation of this collaborative project.

If you are interested in applying, please contact Dr Jiangtao Wang in the first instance.

Sensor-equipped smartphones and wearables devices are transforming the way of health monitoring. Building a machine learning model by leveraging the multiple mobile/IoT devices without leaking their sensitive information becomes a crucial problem. Federated Learning (FL) enables end devices to collaboratively learn a shared prediction model while keeping all the training data locally, which is regarded a good solution to tackle the privacy challenge in many applications including the mobile health scenarios.

However, to keep the communication cost between mobile device and central server in a manageable scale, FL needs to significantly increase the local computing (i.e., model updates) as the sacrifice. By focusing on specific types of mobile/IoT health applications, this PhD project will develop novel FL-based algorithms and systems to balance the factors of energy consumption, communication cost, privacy preserving, model generalizability, and explainability.

We are looking for highly motivated candidates who have a strong programming skill and has a good understanding of AI and deep learning models. The PhD project requires working with computer scientists and AI researchers at different stages along the development pathway – and will lead to high-quality publications. It will involve clinical data collection and analysis, and a variety of computing work.

Entry criteria for applicants to PHD 

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average. 


  • the potential to engage in innovative research and to complete the PhD within a 3.5 years
  • a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)
  • All applications require full supporting documentation, CV, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project. 

For further details see:

For more info please contact

Funding Notes

Bursary plus tuition fees - UK/EU/International

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