@inproceedings{ece26ab74aab49bc816f8cbd4ccda618,
title = "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.",
keywords = "Cryptocurrency Market Prediction, Data Security, Federated Learning, Hyperledger Fabric, Sentiment Analysis",
author = "Zijie Wang and Ziyi Guo and Wanxin Li and Jie Zhang and Hao Guo",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 8th International Conference on Artificial Intelligence and Big Data, ICAIBD 2025 ; Conference date: 23-05-2025 Through 26-05-2025",
year = "2025",
doi = "10.1109/ICAIBD64986.2025.11081867",
language = "English",
series = "2025 8th International Conference on Artificial Intelligence and Big Data, ICAIBD 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "110--115",
booktitle = "2025 8th International Conference on Artificial Intelligence and Big Data, ICAIBD 2025",
}