FIND: Privacy-Enhanced Federated Learning for Intelligent Fake News Detection

• Published in: IEEE transactions on computational social systems 2024-08, Vol.11 (4), p.5005-5014
• Main Authors: Lian, Zhuotao, Zhang, Chen, Su, Chunhua, Dharejo, Fayaz Ali, Almutiq, Mutiq, Memon, Muhammad Hammad
• Format: Article
• Language: English
• Subjects: Data models; Data privacy; Differential privacy (DP); Fake news; fake news detection; Feasibility studies; Federated learning; federated learning (FL); Machine learning; News; Perturbation methods; Privacy; privacy-preservation; Security; Servers; social media; Social networking (online); Social networks; Training
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2023.3304649

The development and popularity of social networks have made information dissemination unprecedentedly convenient and speedy. However, the spread of fake news can often cause serious harm to society and individuals. Therefore, machine learning-based fake news detection methods have become increasingly important. The existing work often needs to collect sufficient user-side data for training, which also boosts the privacy leakage risk to the users. Therefore, this article proposes an intelligent fake news detection system based on federated learning (FL) called FIND, which can train a global model while keeping user data locally. At the same time, we also designed a sparsified update perturbation method to enhance the system security further. Finally, we conduct simulation experiments to study and discuss multiple acoustic factors and prove the feasibility of our system in terms of accuracy, security, and efficiency.