Optimizing Heterogeneity in IoT Infra Using Federated Learning and Blockchain-based Security Strategies

The Internet of Things (IoT) and associated capabilities are becoming indispensable in the planning, operation, and administration of intricate systems of all sizes. High-end learning solutions that go beyond the boundaries of the problem are necessary for addressing the variety of communication concerns (compatibility, secure communication, etc.) in IoT settings. Building machine learning (ML) networks from disparate data sources is a cutting-edge practice known as Federated Learning (FL). In this article, we implement FL between edge-based servers and devices in a sparsely populated cloud to facilitate cohesive learning and the storage of critical information in smart IoT systems. FL enables collaborative training from a common model by aggregating smaller unit models via regulated edge network participants. Further, all the susceptible device's information and sensitive message transactions are addressed via blockchain technology. Thus, a blockchain-based security mechanism is integrated to secure user privacy and facilitate widespread practical adoption. Finally, a comparison is made between the proposed model and the three best free, open-source Federated Learning models already in use (FedPD, FedProx, and FedAvg). In terms of statistical, and data heterogeneity (>70% SDI, >97% accuracy), the experimental findings suggest that the proposed model performs better than the existing techniques.

Automated summary

The Internet of Things (IoT) and its associated capabilities are crucial in complex systems of all sizes. Federated Learning (FL), a cutting-edge practice in machine learning (ML), allows collaborative training from different data sources. This article combines FL and blockchain technology in smart IoT systems to enhance privacy and practical adoption, with comparative results showing the superiority of the proposed model.