Biblio

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.

Sharding is considered to be the most promising solution to overcome and to improve the scalability limitations of blockchain networks. By doing this, the transaction throughput increases, at the same time compromises the security of blockchain networks. In this paper, a probability distribution model is proposed to analyze this trade-off between scalability and security of sharding-based blockchain networks. For this purpose hypergeometric distribution and Chebyshev's Inequality are mainly used. The upper bounds of hypergeometric distributed transaction processing and failure probabilities for shards are mainly evaluated. The model validation is accomplished with Class A (Omniledger, Elastico, Harmony, and Zilliqa), and Class B (RapidChain) sharding protocols. This validation shows that Class B protocols have a better performance compared to Class A protocols. The proposed model observes the transaction processing and failure probabilities are increased when shard size is reduced or the number of shards increased in sharding-based blockchain networks. This trade-off between the scalability and the security decides on the shard size of the blockchain network based on the real-world application and the blockchain platform. This explains the scalability trilemma in blockchain networks claiming that decentralization, scalability, and security cannot be met at primary grounds. In conclusion, this paper presents a comprehensive analysis providing essential directions to develop sharding protocols in the future to enhance the performance and the best-cost benefit of sharing-based blockchains by improving the scalability and the security at the same time.