Secure Sentiment Analysis of Stock News Via Blockchain-Integrated Federated Learning

This study introduces an innovative method to tackle the complexities of ensuring secure and verifiable sentiment analysis of stock news using a combination of blockchain and federated learning integration. The proposed hybrid framework connects recurrent neural networks (RNNs) for sentiment analysis. It employs blockchain-based smart contracts for model verification, thereby upholding the confidentiality and integrity of the federated learning process. Within the federated learning network, each participating node independently trains a local RNN model on its specific set of stock news articles, protecting data privacy. The accumulation of model updates is achieved through secure federated averaging, and the blockchain network consensus mechanisms validate the authenticity of the aggregated model. Automating the verification process via smart contracts allows only valid updates to be accepted and recorded on the blockchain ledger. The transparency and immutability of the blockchain enable stakeholders to scrutinize the federated learning procedure and ensure the dependability of the sentiment analysis results. Our approach is assessed through simulations and experiments, illustrating its efficacy in maintaining data privacy, preventing adversarial attacks, and imparting confidence in the sentiment analysis of stock news.