Federated Learning-Aware Multi-Objective Modeling and blockchain-enable system for IIoT applications

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.