ABSTRACT
As social media becomes more and more popular, fake news spreads rapidly which is more likely to cause serious consequences, especially during the COVID-19 pandemic. On the premise of meeting data privacy and security requirements, federated learning uses multi-party heterogeneous data to further promote machine learning. This paper proposes a federal learning based COVID-19 fake news detection model with deep self-attention network (FL-FNDM). We construct a deep self-attention network for fake news detection, which combines self-attention-based pretrained model BERT and deep convolutional neural network to detect fake news. Moreover, the fake news detection model is learned under the framework of horizontal federated learning, aiming at protecting users' data security and privacy. The experimental results demonstrate that the proposed model can improve the performance of fake news detection on the COVID-19 dataset, which can achieve almost the same effect of sharing data without leaking user data. © 2021 IEEE.