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Heterogeneous Data-Aware Federated Learning for Medical AI

Funding:

Self-funded PhD

Duration:

3.5 (full-time) 5 years (part-time)

Application deadline:

Applications accepted year-round

About this project

Federated learning is a technique that allows the decentralized training of machine learning models while preserving the user's privacy. As a result, federated learning brings the promise of providing a privacy-preserving mechanism for analyzing healthcare datasets. Yet, in their current implementations, federated learning algorithms assume independent and identically distributed (iiD) datasets. As a result, they might fail when facing real-world healthcare datasets where data is gathered from heterogeneous non-iiD sources. To address this issue, this project aims to investigate and develop strategies to deal with non-iiD data especially when applying federated learning to healthcare domains.

How to apply

Apply online.

Please include the project title and proposed supervisor in your application.

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Funding

Open to fully self-funded full time / part time students only.

Entry requirements

Applicants should have a minimum 2:1 degree in Computer Science or related subject. A taught MSc or Masters by Research in a relevant subject or relevant laboratory experience would be an advantage.

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