335 citations · 583 across the 23 of their papers we have counts for
6 papers · 1 filter
Contribution Evaluation in Federated Learning: Examining Current Approaches
Vasilis Siomos, Jonathan Passerat-Palmbach
Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data. In FL, c…
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…
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments
Harry Cai, Daniel Rueckert, Jonathan Passerat-Palmbach
Federated Learning harnesses data from multiple sources to build a single model. While the initial model might belong solely to the actor bringing it to the network for training, d…
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl +4
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valu…