190 citations · 566 across the 19 of their papers we have counts for
3 papers · 1 filter
FLaaS: Federated Learning as a Service
Nicolas Kourtellis, Kleomenis Katevas, Diego Perino
Federated Learning (FL) is emerging as a promising technology to build machine learning models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on…
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas +4
We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neur…
Policy-Based Federated Learning
Kleomenis Katevas, Eugene Bagdasaryan, Jason Waterman +4
In this paper we present PoliFL, a decentralized, edge-based framework that supports heterogeneous privacy policies for federated learning. We evaluate our system on three use case…