Towards Efficiency and Decentralization: A Blockchain Assisted Distributed Fuzzy-Rough Feature Selection

作者：Lin Qiu, Xingwei Wang, Bo Yi, Kaimin Zhang, Fei Gao, Min Huang, Yanpeng Qu · 发表于：IEEE Transactions on Parallel and Distributed Systems · 年份：2025 · DOI：10.1109/tpds.2025.3578032 · 被引用次数：3 · 研究领域：Blockchain Technology Applications and Security、Rough Sets and Fuzzy Logic、Imbalanced Data Classification Techniques

Fuzzy-rough sets-based feature selection (FRFS), as an effective data pre-processing technique, has drawn significant attention with the growing prevalence of large-scale datasets. However, centralized FRFS approaches suffer from the following shortcomings: 1) low computational efficiency, 2) bottlenecks in memory and computational resources, and 3) strict limitation of collaborative implementation using nonshared datasets owned by different data providers. These limitations highlight the growing necessity of integrating FRFS into a distributed FS framework. Nevertheless, most existing distributed FS schemes are reliant on a designated central server to collect and merge the local results from all slave nodes, which may result in several challenges including single point of failure risk, lack of trust and reliability, and lack of transparency and traceability. To relieve the above issues, this paper proposes a blockchain assisted distributed FS framework, successfully implementing a distributed solution for FRFS (BDFRFS). Firstly, this framework introduces blockchain to merge, reach consensus and publish the global results generated during each iteration of FRFS, including the currently selected feature subset with its corresponding similarity matrix and dependency degree. This not only eliminates the reliance of central server and alleviates the burden on the central server, but also enhances the credibility and traceability of the results. Additionally, the implementation o...