f-FNC: Privacy concerned efficient federated approach for fake news classification

Fake news and manipulated information affect the social, economic and emotional growth of the world's population. For the identification of fake news, several classification systems are available, but no such system was found fast, secure and reliable as per the need of the hour. In this work, an efficient framework based on the federated architecture for the classification of fake news was proposed, while maintaining the data privacy constraints for sensitive text news datasets. The proposed federated-Fake New Classification (f-FNC) framework utilized the distributed client–server architecture with data privacy of all client or connected edge devices. For the testing and evaluation of the proposed f-FNC framework, the non-identical data was gathered from several online resources and was disseminated in a pre-processed format. To test the validity of federated deep learning models, the experiments were performed under various scenarios such as traditional learning, federated learning single client, and federated learning multi-clients. The performance of f-FNC framework was evaluated through various computational parameters such as accuracy and loss validation along with available resource parameters including CPU and RAM utilization. It was observed from the resultant outcome that the proposed f-FNC framework worked significantly well in both single-client and multi-client (N-clients) scenarios in comparison to traditional distributed deep learning based classifiers. The additional features of low cost and data-privacy of edge devices with limited resources made this proposed framework unique and the best alternative to existing fake news classifier tools.