13 citations · 53 across the 13 of their papers we have counts for
3 papers · 1 filter
Compression Boosts Differentially Private Federated Learning
Raouf Kerkouche, Gergely Ács, Claude Castelluccia +1
Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by excha…
Federated Learning in Adversarial Settings
Raouf Kerkouche, Gergely Ács, Claude Castelluccia
Federated Learning enables entities to collaboratively learn a shared prediction model while keeping their training data locally. It prevents data collection and aggregation and, t…
In Search of Lost Utility: Private Location Data
Szilvia Lestyán, Gergely Ács, Gergely Biczók
The unavailability of training data is a permanent source of much frustration in research, especially when it is due to privacy concerns. This is particularly true for location dat…