Unleashing the prospective of blockchain-federated learning fusion for IoT security: A comprehensive review

Abstract

Internet-of-things (IoT) is a revolutionary paragon that brings automation and easiness to human lives and improves their experience. Smart Homes, Healthcare, and Agriculture are some of their amazing use cases. These IoT applications often employ Machine Learning (ML) techniques to strengthen their functionality. ML can be used to analyze sensor data for various, including optimizing energy usage in smart homes, predicting maintenance needs in industrial equipment, personalized user experiences in wearable devices, and detecting anomalies for security monitoring. However, implementing centralized ML techniques is not viable because of the high cost of computing power and privacy issues since so much data is stored over a cloud server. To safeguard data privacy, Federated Learning (FL) has become a new paragon for centralized ML methods where FL,an ML variation sends a model to the user devices without the need to give private data to the third-party or central server, it is one of the promising solutions to address data leakage concerns. By saving raw data to the client itself and transferring only model updates or parameters to the central server, FL helps to reduce privacy leakage. However, it is still not attack-resistant. Blockchain offers a solution to protect FL-enabled IoT networks using smart contracts and consensus mechanisms. This manuscript reviews IoT applications and challenges, discusses FL techniques that can be used to train IoT networks while ensuring privacy, and analyzes existing work. To ensure the security and privacy of IoT applications, an integrated Blockchain-powered FL-based framework was introduced and studies existing research were done using these three powerful paradigms. Finally, the research challenges faced by the integrated platform are explored for future scope, along with the potential applications of IoT in conjunction with other cutting-edge technologies.