Blockchain and federated learning-based distributed computing defence framework for sustainable society

Abstract

Ensuring social security through the defense organization determines the creation of links between the army and society. Realizing the benefits of the Internet of Battle Things in the defense system can perfectly monetize intelligence and strengthen the armed forces. It establishes a network for strong connectivity in the army with good coordination between complex processes to effectively edge out the enemies. However, this new technology poses organizational and national security challenges that present both opportunities and obstacles. The current framework of the defense IoT network for sustainable society is not adequate enough to make actionable situational awareness decisions in order to infer the state of the battlefield while preserving the privacy of sensitive data. In this paper, we propose a distributed computing defence framework for sustainable society using the features of blockchain technology and federated learning. The proposed model presents an algorithm to meet the challenges of limited training data in order to obtain high accuracy and avoid a reason specific model. To evaluate the effectiveness of the proposed model, we prepare the dataset and investigate the performance of our framework in various scenarios. The result outcomes are promising in terms of accuracy and loss compared to baseline approach.