Federated Learning on Blockchain Networks

This paper contributes to ground-breaking advancement in the ever-expanding realms of federated learning on blockchain data towards analytics for forecasting and predictions using the LSTM deep learning algorithm. An incremental progression is achieved by extracting blockchain blocks and communicating between decentralized blockchains, cloud, and edge networks. One of the key takeaways from the approach fostered in this article is a purpose that pervaded a global federated model with analytics for forecasting's and recommendations across blockchain networks as an endeavor. It is an exceptional experience of connecting vast and dynamic networks to unveil patterns and enhancing explorations to leave an indelible mark of predictions over data with endless possibilities. The driving objective behind the multi-vector push of this work is ensuring gratified recommendations and helping users with fortitude in decision-making and power shift to profits.