808 citations · 876 across the 9 of their papers we have counts for
3 papers · 1 filter
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation
Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem +4
We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data bec…
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…
How To Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua +2
Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphon…