Fake News Detection using a Decentralized Deep Learning Model and Federated Learning | IEEE Conference Publication | IEEE Xplore

Fake News Detection using a Decentralized Deep Learning Model and Federated Learning


Abstract:

Social media has beneficial and detrimental impacts on social life. The vast distribution of false information on social media has become a worldwide threat. As a result,...Show More

Abstract:

Social media has beneficial and detrimental impacts on social life. The vast distribution of false information on social media has become a worldwide threat. As a result, the Fake News Detection System in Social Networks has risen in popularity and is now considered an emerging research area. A centralized training technique makes it difficult to build a generalized model by adapting numerous data sources. In this study, we develop a decentralized Deep Learning model using Federated Learning (FL) for fake news detection. We utilize an ISOT fake news dataset gathered from "Reuters.com" (N = 44,898) to train the deep learning model. The performance of decentralized and centralized models is then assessed using accuracy, precision, recall, and F1-score measures. In addition, performance was measured by varying the number of FL clients. We identify the high accuracy of our proposed decentralized FL technique (accuracy, 99.6%) utilizing fewer communication rounds than in previous studies, even without employing pre-trained word embedding. The highest effects are obtained when we compare our model to three earlier research. Instead of a centralized method for false news detection, the FL technique may be used more efficiently. The use of Blockchain-like technologies can improve the integrity and validity of news sources.
Date of Conference: 17-20 October 2022
Date Added to IEEE Xplore: 09 December 2022
ISBN Information:

ISSN Information:

Conference Location: Brussels, Belgium

Contact IEEE to Subscribe

References

References is not available for this document.