【Author】
Lakhan, Abdullah; Mohammed, Mazin Abed; Kadry, Seifedine; AlQahtani, Salman A.; Maashi, Mashael S.; Abdulkareem, Karrar Hameed
【Source】COMPUTERS & ELECTRICAL ENGINEERING
【Abstract】The study devises the Federated Learning Aware Multi-Objective Modeling in Decentralized Microservices Assisted IIoT System. Energy consumption and application delay have been taken as the study's objectives. The system proposes different schemes, such as Deadline Latency Energy. The work devises the Blockchain-Enabled Federated Learning Algorithm Framework (DLEBAF) with different strategies. The first strategy is deadline-efficient task sequencing and scheduling (DETS), which allocates all applications (workloads) according to their deadline. The second strategy is latency-efficient task scheduling (LETS) to minimize the latency of workloads. The third strategy is energy-efficient task scheduling (EETS), which reduces the energy of fog nodes. The blockchain-enabled fog-cloud (BEFC) scheme ensures the blockchain validation, hashing, previous hash, and time of applications in the system. The results will compare the optimal energy results and delay existing studies with the proposed work. Results showed that the proposed method improves by 30% energy and 50% training delay of all applications.
【Keywords】EETS; DETS; BEFC; Microservice; IIoT; Blockchain; Multi-objectives; Fog; Cloud; Simulation
【标题】用于 IIoT 应用程序的联邦学习感知多目标建模和区块链启用系统
【摘要】该研究设计了分布式微服务辅助 IIoT 系统中的联邦学习感知多目标建模。能源消耗和应用延迟已作为研究的目标。该系统提出了不同的方案，例如Deadline Latency Energy。这项工作设计了具有不同策略的支持区块链的联邦学习算法框架（DLEBAF）。第一个策略是截止期限有效的任务排序和调度 (DETS)，它根据截止期限分配所有应用程序（工作负载）。第二种策略是延迟有效的任务调度 (LETS)，以最大限度地减少工作负载的延迟。第三个策略是节能任务调度（EETS），它降低了雾节点的能量。支持区块链的雾云（BEFC）方案确保了系统中应用程序的区块链验证、散列、先前的散列和时间。结果将比较最佳能量结果并延迟现有研究与拟议工作。结果表明，所提出的方法提高了所有应用程序的 30% 能量和 50% 的训练延迟。
【关键词】EETS；检测； BEFC;微服务；工业物联网；区块链；多目标；多雾路段;云;模拟
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