A novel Internet of Things and federated learning-based privacy protection in blockchain technology

Abstract

Purpose Be that as it may, BC is computationally costly, has restricted versatility and brings about critical transmission capacity upward and postpones, those seems not to be fit with Internet of Things (IoT) setting. A lightweight scalable blockchain (LSB) which is improved toward IoT necessities is suggested by the authors and investigates LSB within brilliant house setup like an agent model to enable more extensive IoT apps. Less asset gadgets inside brilliant house advantage via any unified chief which lays out common units for correspondence also cycles generally approaching and active solicitations. Design/methodology/approach Federated learning and blockchain (BC) have drawn in huge consideration due to the unchanging property and the relevant safety measure and protection benefits. FL and IoT safety measures’ difficulties can be conquered possibly by BC. Findings LSB accomplishes fragmentation through shaping any overlaid web with more asset gadgets mutually deal with a public BC and federated learning which assures complete protection also security. Originality/value This overlaid is coordinated as without error bunches and reduces extra efforts, also batch leader will be with answer to handle commonly known BCs. LSB joins some of advancements which also includes computations related to lesser weighing agreement, optimal belief also throughput regulatory body.