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

Zijie Wang; Ziyi Guo; Wanxin Li; Jie Zhang; Hao Guo

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

Zijie Wang, Ziyi Guo, Wanxin Li^*, Jie Zhang^*, Hao Guo^*

^*Corresponding author for this work

Department of Communications and Networking

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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.

Original language	English
Title of host publication	IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD 2025)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Publication status	Accepted/In press - May 2025

Cite this

@inproceedings{a1846b59ab5f4d22bc3ad0e0341e23f2,

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.",

author = "Zijie Wang and Ziyi Guo and Wanxin Li and Jie Zhang and Hao Guo",

year = "2025",

month = may,

language = "English",

booktitle = "IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD 2025)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Integrating Blockchain and Federated Learning for Cryptocurrency Market Prediction: Major Exchanges as Nodes. / Wang, Zijie; Guo, Ziyi; Li, Wanxin et al.
IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD 2025). Institute of Electrical and Electronics Engineers Inc., 2025.

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Integrating Blockchain and Federated Learning for Cryptocurrency Market Prediction: Major Exchanges as Nodes

AU - Wang, Zijie

AU - Guo, Ziyi

AU - Li, Wanxin

AU - Zhang, Jie

AU - Guo, Hao

PY - 2025/5

Y1 - 2025/5

N2 - 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.

AB - 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.

M3 - Conference Proceeding

BT - IEEE International Conference on Artificial Intelligence and Big Data (ICAIBD 2025)

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

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

Abstract

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