【Author】
Wang, Ke; Chen, Chien-Ming; Liang, Zuodong; Hassan, Mohammad Mehedi; Sarne, Giuseppe M. L.; Fotia, Lidia; Fortino, Giancarlo
【Source】INFORMATION FUSION
【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
【标题】海量物联网领域去中心化协作学习的可信共识融合方案
【摘要】在海量物联网系统中，大量数据被收集并存储在云端、边缘设备和终端中，但数据大多是孤立的。对于各种智能应用的许多新需求，对这些数据进行自组织协作学习以实现群体决策已经成为一种新趋势。然而，为了达到群体决策的目标，需要解决数据融合和模型融合的信任问题，因为参与者可能不被信任。我们提出与联盟链一致、信任融合的方法，达成共识，完成自组织的可信去中心化协作学习。在每个共识过程中，共识候选人检查其他人的信任级别，以确保他们倾向于与具有高信任度的用户融合共识，信任级别根据他们在过去共识过程中的历史行为进行评分评估，并存储在区块链的公共账本中。设计了一种信任奖惩方法来实现信任激励共识，信任度高的候选人在共识中拥有更多的权利和声誉。仿真结果和安全性分析表明，该方法能够有效防御恶意用户和数据，提高整个联邦学习网络的信任感知性能，使联邦学习更加可信和稳定。
【关键词】共识融合；信任评估；区块链；联盟链；协作学习
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