【Author】
Sharma, Pradip Kumar; Park, Jong Hyuk; Cho, Kyungeun
【Source】SUSTAINABLE CITIES AND 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.
【Keywords】Distributed computing; Internet of battle things; Sustainable society; Blockchain; Federated learning
【标题】面向可持续社会的基于区块链和联邦学习的分布式计算防御框架
【摘要】通过国防组织确保社会保障决定了军队与社会之间建立联系。在国防系统中实现战斗物联网的好处，可以完美地将情报货币化，增强武装力量。它在军队中建立了一个强大的连接网络，在复杂过程之间进行了良好的协调，从而有效地排除了敌人。然而，这项新技术带来了组织和国家安全挑战，既带来机遇，也带来障碍。当前的可持续社会防御物联网网络框架不足以做出可操作的态势感知决策，以便在保护敏感数据隐私的同时推断战场状态。在本文中，我们利用区块链技术和联邦学习的特点，为可持续社会提出了分布式计算防御框架。所提出的模型提出了一种算法来应对有限训练数据的挑战，以获得高精度并避免特定原因的模型。为了评估所提出模型的有效性，我们准备了数据集并研究了我们的框架在各种场景中的性能。与基线方法相比，结果结果在准确性和损失方面是有希望的。
【关键词】分布式计算；战物联网；可持续社会；区块链；联邦学习
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