Integrating Blockchain and Federated Learning for Cryptocurrency Market Prediction: Major Exchanges as Nodes

Abstract

This study introduces a framework that integrates the Hyperledger Fabric blockchain with federated learning to improve cryptocurrency market prediction. Using three major exchanges as distributed nodes, the platform processes trading data and sentiment analysis locally, training machine learning models on each node. The results show that the federated model achieves a prediction deviation of 0. 65% from the actual prices, exceeding the deviation of the centralized LSTM model of 3. 35%. The Hyperledger Fabric network also handles up to 298.7 TPS with zero transaction failures and low latency (0.01s), highlighting the model’s effectiveness for secure and accurate market prediction in the fintech sector.