A trusted consensus fusion scheme for decentralized collaborated learning in massive IoT domain

【Author】 Wang, Ke; Chen, Chien-Ming; Liang, Zuodong; Hassan, Mohammad Mehedi; Sarne, Giuseppe M. L.; Fotia, Lidia; Fortino, Giancarlo

【Source】INFORMATION FUSION

【影响因子】17.564

【Abstract】In a massive IoT systems, large amount of data are collected and stored in clouds, edge devices, and terminals, but the data are mostly isolated. For many new demands of various intelligent applications, self-organized collaborated learning on those data to achieve group decisions has been a new trend. However, in order to reach the goal of group decisions, trust problems on data fusion and model fusion should be solved since the participants may not be trusted. We propose a consistent and trust fusion method with the consortium chain to reach a consensus, and complete the self-organized trusted decentralized collaborated learning. In each consensus process, consensus candidates check others? trust levels to ensure that they tends to fuse consensus with users with high trust, where the trust levels are evaluated by scores according to their historical behaviors in the past consensus process and stored in the public ledger of blockchain. A trust rewards and punishments method is designed to realize trust incentive consensus, the candidates with higher trust levels have more rights and reputation in the consensus. Simulation results and security analysis show that the method can effectively defend malicious users and data, improve the trust perception performance of the whole federated learning network, and make the federated learning more trusted and stable.

【Keywords】Consensus fusion; Trust evaluation; Blockchain; Consortium chain; Collaborated learning

【发表时间】2021 AUG

【收录时间】2022-01-02

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-物联网