Hybrid Cloud–Edge Architecture for Real-Time Cryptocurrency Market Forecasting: A Distributed Machine Learning Approach with Blockchain Integration

Mohammed M. Alenazi () and Fawwad Hassan Jaskani
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Mohammed M. Alenazi: Department of Computer Engineering, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71421, Saudi Arabia
Fawwad Hassan Jaskani: Department of Computer Systems Engineering, Faculty of Engineering, Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

Mathematics, 2025, vol. 13, issue 18, 1-26

Abstract: The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions.

Keywords: cryptocurrency forecasting; cloud–edge computing; distributed machine learning; blockchain analytics; federated learning; real-time prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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