190 citations · 307 across the 10 of their papers we have counts for
15 papers
BatteryLab: A Collaborative Platform for Power Monitoring
Matteo Varvello, Kleomenis Katevas, Mihai Plesa +3
Advances in cloud computing have simplified the way that both software development and testing are performed. This is not true for battery testing for which state of the art test-b…
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis +2
Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms…
PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas +3
We propose and implement a Privacy-preserving Federated Learning () framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread pr…
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…