The performance of artificial intelligence based learning models is often initially assessed using well established benchmark datasets. However, satisfactory performance on these datasets does not guarantee similar performance in real-world settings where the data may be significantly more imbalanced. For example, in a medical dataset a positive diagnosis may be a relatively rare event and will, therefore, form a minority class within the dataset.
One form of learning across multiple organisations/devices/clients is Federated Learning (FL). It is a mechanism for efficient, collaborative training of a shared model; where participating clients (e.g. edge nodes, sensors, devices) cooperate to train a model by exchanging trained parameters with a central server that coordinates the learning process. The security and privacy of the original data is enhanced as it is not shared with the central server.
This project will explore the use of federated learning with imbalanced data sets, where the focus of the learning is on the identification of minority classes within the dataset.
You need to be comfortable working with maths, networking and programming to undertake this project.