Federated Learning for Adverse Drug Reaction Prediction

This paper addresses the problem of biased data in adverse drug reaction (ADR) prediction, a critical issue in healthcare. The authors propose a federated learning approach, PFed-Signal, to mitigate the impact of biased data in the FAERS database. The use of Euclidean distance for biased data identification and a Transformer-based model for prediction are novel aspects. The paper's significance lies in its potential to improve the accuracy of ADR prediction, leading to better patient safety and more reliable diagnoses.