808 citations · 876 across the 9 of their papers we have counts for
9 papers
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…
Creative Procedural-Knowledge Extraction From Web Design Tutorials
Longqi Yang, Chen Fang, Hailin Jin +2
Complex design tasks often require performing diverse actions in a specific order. To (semi-)autonomously accomplish these tasks, applications need to understand and learn a wide r…
Augmenting Gastrointestinal Health: A Deep Learning Approach to Human Stool Recognition and Characterization in Macroscopic Images
David Hachuel, Akshay Jha, Deborah Estrin +3
Purpose - Functional bowel diseases, including irritable bowel syndrome, chronic constipation, and chronic diarrhea, are some of the most common diseases seen in clinical practice.…
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…
Internet Scale Research Studies using SDL-RX
James Kizer, Arnaud Sahaguet, Neil Lakin +3
Medical research is one area where collecting data is usually hard and expensive. With the launch of ResearchKit, Apple and Sage Bionetworks made large-scale personal data collecti…