FusionFedBlock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0 br

被引:36
|
作者
Singh, Sushil Kumar [1 ]
Yang, Laurence T. [2 ]
Park, Jong Hyuk [1 ]
机构
[1] Seoul Natl Univ Sci & Technol, SeoulTech, Dept Comp Sci & Engn, Seoul 01811, South Korea
[2] St Francis Xavier Univ, Dept Math Stat & Comp Sci, Antigonish, NS, Canada
基金
新加坡国家研究基金会;
关键词
Blockchain; Federated learning; Information fusion; Privacy; -preservation; Industrial IoT; Industry; 5; 0; Security; OPPORTUNITIES; NETWORKS; IOT;
D O I
10.1016/j.inffus.2022.09.027
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Nowadays, Industries are experiencing rapid changes in the digital environment, referred to as Industry 5.0. The Internet of Things (IoT) and advanced technologies are essential in the industrial environment. Technological advancements can collect, transfer, and analyze vast amounts of data in the industry via promising technologies. Still, IoT has various issues when applied to industrial infrastructures, such as centralization, privacy preservation, latency, and security. This article proposes a scheme as FusionFedBlock: Fusion of Blockchain and Federated Learning to Preserve Privacy in Industry 5.0 to address the aforementioned issues. At the federated layer, the industry's departments (Production, Quality Control, Distribution) allow local learning updates with network automation and communicate to the global model, which miners verify in the Blockchain networks. Federated-Learning offers privacy preservation between various mentioned departments in industries. Decentralized secure storage is provided by the Distributed Hash Table (DHT) at the cloud layer. The validation outcomes of the proposed scheme demonstrate excellent performance as the accuracy of 93.5% in a 50% active node for Industry 5.0 compared to existing frameworks.
引用
收藏
页码:233 / 240
页数:8
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