55 citations · 285 across the 20 of their papers we have counts for
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cs.LG2020
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…
cs.LG2017
Differentially Private Mixture of Generative Neural Networks
Gergely Acs, Luca Melis, Claude Castelluccia +1
Generative models are used in a wide range of applications building on large amounts of contextually rich information. Due to possible privacy violations of the individuals whose d…