Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT.

Arumugam K,Sudhanshu Maurya,Mounir Hamdi,Anju Asokan,Abdullah A Algethami,Poongodi M,Hafiz Tayyab Rauf,Senoj Joseph,Srimathi J

doi:10.3390/s21237793

Abstract

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.

Highlights

Nowadays, essential applications of IoT have created great opportunities for industrial innovations [1]
The foremost category of the transfer utilizes the networks by training them for Supportive Twin Delayed Deep Deterministic Policy Gradient (DDPG) (S-TD3), facilitating user authentication belonging to the category of foreign users
All industrial applications in the Internet of Things require reliable and real-time information that helps in user interaction and becomes vulnerable to several types of illegal attacks, information leakage, and denying services

Summary

Introduction

Essential applications of IoT have created great opportunities for industrial innovations [1] It integrates mobile communications, cloud computing, and artificial intelligence into all elements of the production process. Based on the above analysis, the IIoT architecture is constructed via empowerment by edge and via intelligence and employment of the authentication process based on TL This is performed in order to build trustworthy blockchains and intelligence. The edge server is a component of the edge intelligent network, blockchain system, and artificial intelligence [11] They are responsible for analysis, fusion, and processing of the data; for providing security to data; and for protecting its privacy for many IIoT applications. With the help of transfer learning of the outer blockchains, the successful transfer of the models of authentication is accurately carried out from the local level to foreign users. With the help of transfer learning of the outer blockchains, the successful transfer of the models of authentication is accurately carried out from the local level to foreign users. The final results of these are (i) that there is accurate authentication for local as well as foreign users, and (ii) that higher throughput and low latency are achieved in several scenarios of IIoT

Related Work

Performance Metrics

Results

Conclusions and Future Work