Siwan Noh¹, Kyung-Hyune Rhee^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3805-3826, 2024, DOI:10.32604/cmc.2024.050949

Abstract In Decentralized Machine Learning (DML) systems, system participants contribute their resources to assist others in developing machine learning solutions. Identifying malicious contributions in DML systems is challenging, which has led to the exploration of blockchain technology. Blockchain leverages its transparency and immutability to record the provenance and reliability of training data. However, storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs. Additionally, current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data. However, less… More >

Ba-Lam Do^*, Van-Thanh Nguyen, Hoang-Nam Dinh, Thanh-Chung Dao, BinhMinh Nguyen

Computer Systems Science and Engineering, Vol.43, No.2, pp. 591-604, 2022, DOI:10.32604/csse.2022.023508

Abstract In recent years, blockchain technology has been applied in the educational domain because of its salient advantages, i.e., transparency, decentralization, and immutability. Available systems typically use public blockchain networks such as Ethereum and Bitcoin to store learning results. However, the cost of writing data on these networks is significant, making educational institutions limit data sent to the target network, typically containing only hash codes of the issued certificates. In this paper, we present a system based on a private blockchain network for lifelong learning data authentication and management named B4E (Blockchain For Education). B4E stores… More >