Nakamoto Consensus to Accelerate Supervised Classification Algorithms for Multiparty Computing

• Main Authors: Zhen Zhang, Bing Guo, Yan Shen, Chengjie Li, Xinhua Suo, Hong Su
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Security and Communication Networks
• Online Access: http://dx.doi.org/10.1155/2021/6629433

Bitcoin mining consumes tremendous amounts of electricity to solve the hash problem. At the same time, large-scale applications of artificial intelligence (AI) require efficient and secure computing. There are many computing devices in use, and the hardware resources are highly heterogeneous. This means a cooperation mechanism is needed to realize cooperation among computing devices, and a good calculation structure is required in the case of data dispersion. In this paper, we propose an architecture where devices (also called nodes) can reach a consensus on task results using off-chain smart contracts and private data. The proposed distributed computing architecture can accelerate computing-intensive and data-intensive supervised classification algorithms with limited resources. This architecture can significantly increase privacy protection and prevent leakage of distributed data. Our proposed architecture can support heterogeneous data, making computing on each device more efficient. We used mathematical formulas to prove the correctness and robustness of our system and deduced the condition to stop a given task. In the experiments, we transformed Bitcoin hash collision into distributed computing on several nodes and evaluated the training and prediction accuracy for handwritten digit images (MNIST). The experimental results demonstrate the effectiveness of the proposed method.