94 citations · 204 across the 8 of their papers we have counts for
3 papers · 1 filter
A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses
Caelin G. Kaplan, Chuan Xu, Othmane Marfoq +2
Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without…
Federated Learning under Heterogeneous and Correlated Client Availability
Angelo Rodio, Francescomaria Faticanti, Othmane Marfoq +2
The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collabora…
Federated Learning for Data Streams
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni +1
Federated learning (FL) is an effective solution to train machine learning models on the increasing amount of data generated by IoT devices and smartphones while keeping such data…