335 citations · 583 across the 23 of their papers we have counts for
3 papers · 1 filter
Distributed Machine Learning and the Semblance of Trust
Dmitrii Usynin, Alexander Ziller, Daniel Rueckert +2
The utilisation of large and diverse datasets for machine learning (ML) at scale is required to promote scientific insight into many meaningful problems. However, due to data gover…
FedRAD: Federated Robust Adaptive Distillation
Stefán Páll Sturluson, Samuel Trew, Luis Muñoz-González +4
The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients. The collaborative learning…
Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques
Ashly Lau, Jonathan Passerat-Palmbach
Differential privacy provides strong privacy guarantees for machine learning applications. Much recent work has been focused on developing differentially private models, however th…