Federated Analysis
Our goal is to model problems using federated learning (FL) on decentralised, heterogeneous data to the same (or acceptable) accuracy as if the data had been available in one centre, while preserving data privacy, or to understand the bounds on when this is possible.
Our research is investigating theoretical aspects of image and video analysis FL architecture design as well as real-world applications of FL.