190 citations · 566 across the 19 of their papers we have counts for
4 papers · 1 filter
FLaaS: Cross-App On-device Federated Learning in Mobile Environments
Kleomenis Katevas, Diego Perino, Nicolas Kourtellis
Federated Learning (FL) has recently emerged as a popular solution to distributedly train a model on user devices improving user privacy and system scalability. Major Internet comp…
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…
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…
Towards Characterizing and Limiting Information Exposure in DNN Layers
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas +2
Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensiti…