University of California, Irvine
Visiting Researcher, California, USA, 2022
- Provide information-theoretic upper bounds on the amount of information that the local model update reveals about any single user’s dataset in a federated learning system.
- Conduct various state-of-the-art model pruning schemes in Federated Learning under various privacy attacks. These experimental evaluations quantify the exact amount of privacy loss in concrete settings and demonstrate alignment with theoretical bounds.
- Design a defence mechanism by applying a personalised defence mask and adapting the defence pruning rate, so as to jointly optimise model accuracy, privacy, and efficiency for low-resource environments.