Poster abstract: Resource hunting on the edge
Ahmed Salem, Nirmit Desai, et al.
SEC 2016
Using electronic health data to predict adverse drug reaction (ADR) incurs practical challenges, such as lack of adequate data from any single site for rare ADR detection, resource constraints on integrating data from multiple sources, and privacy concerns with creating a centralized database from person-specific, sensitive data. We introduce a federated learning framework that can learn a global ADR prediction model from distributed health data held locally at different sites. We propose two novel methods of local model aggregation to improve the predictive capability of the global model. Through comprehensive experimental evaluation using real-world health data from 1 million patients, we demonstrate the effectiveness of our proposed approach in achieving comparable performance to centralized learning and outperforming localized learning models for two types of ADRs. We also demonstrate that, for varying data distributions, our aggregation methods outperform state-of-the-art techniques, in terms of precision, recall, and accuracy.
Ahmed Salem, Nirmit Desai, et al.
SEC 2016
Ting He, Chang Liu, et al.
SIGMETRICS 2015
Tiffany Tuor, Shiqiang Wang, et al.
INFOCOM 2018
Olivia Choudhury, Nolan Rudolph, et al.
iThings/GreenCom/CPSCom/SmartData/Blockchain/CIT 2018