13 citations · 26 across the 10 of their papers we have counts for
10 papers
Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions
Bianca-Mihaela Ganescu, Jonathan Passerat-Palmbach
Generative AI, exemplified by models like transformers, has opened up new possibilities in various domains but also raised concerns about fairness, transparency and reliability, es…
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…
Cooperative AI via Decentralized Commitment Devices
Xinyuan Sun, Davide Crapis, Matt Stephenson +3
Credible commitment devices have been a popular approach for robust multi-agent coordination. However, existing commitment mechanisms face limitations like privacy, integrity, and…
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…
Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016
Sarah Parisot, Jonathan Passerat-Palmbach, Markus D. Schirmer +1
Understanding brain connectivity in a network-theoretic context has shown much promise in recent years. This type of analysis identifies brain organisational principles, bringing a…