A Federated Convolution Transformer for Fake News Detection

We present a novel approach to detect fake news in Internet of Things (IoT) applications. By investigating federated learning and trusted authority methods, we address the issue of data security during training. Simultaneously, by investigating convolution transformers and user clustering, we deal with multi-modality issues in fake news data. First, we use dense embedding and the k-means algorithm to cluster users into groups that are similar to one another. We then develop a local model for each user using their local data. The server then receives the local models of users along with clustering information, and a trusted authority verifies their integrity there. We use two different types of aggregation in place of conventional federated learning systems. The initial step is to combine all users' models to create a single global model. The second step entails compiling each user's model into a local model of comparable users. Both models are supplied to users, who then select the most suitable model for identifying fake news. By conducting extensive experiments using Twitter data, we demonstrate that the proposed method outperforms various baselines, where it achieves an average accuracy of 0.85 in comparison to others that do not exceed 0.81.