Ting He, Chang Liu, et al.
SIGMETRICS 2015
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.
Ting He, Chang Liu, et al.
SIGMETRICS 2015
Apostolos Galanopoulos, Argyrios G. Tasiopoulos, et al.
ICC 2020
Andrew Machen, Shiqiang Wang, et al.
MobiCom 2016
David Wood, Shiqiang Wang, et al.
SPIE Defense + Security 2018