Federated learning is a machine learning approach that enables multiple parties to collaborate in developing a shared model while safeguarding the distribution and privacy of their data. The application of federated learning to critical national infrastructure protection offers several advantages, including enhanced security, efficiency, and privacy. Nevertheless, various security issues and concerns persist. In this Ph.D. research, our focus is on addressing one of these challenges, specifically concentrating on Communication Efficiency within the context of federated learning for Critical National Infrastructure. We aim to discover novel techniques to minimize the volume of communication required between the central server and the participants. Our goal is to reduce the communication overhead while ensuring model security and accuracy.
Academic qualifications
A second class honour degree or equivalent qualification in automation & control, industry 4.0, cybersecurity.
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
Application process
Prospective applicants are encouraged to contact the supervisor, Dr Naghmeh Moradpoor (N.Moradpoor@napier.ac.uk) to discuss the content of the project and the fit with their qualifications and skills before preparing an application.
The application must include:
Research project outline of 2 pages (list of references excluded). The outline may provide details about
The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
Applications can be submitted here.
Download a copy of the project details here.
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