Neural Architecture Search for Robust Networks in 6G-enabled Massive IoT Domain

Ke Wang, Peng Xu, Chien Ming Chen, Saru Kumari, Mohammad Shojafar, Mamoun Alazab

Research output: Contribution to journalArticlepeer-review

Abstract

6G technology enables AI-based massive IoT to manage network resources and data with ultra high speed, responsive network and wide coverage. However, many artificial intelligence-enabled internet of things (AIoT) systems are vulnerable to adversarial example attacks. Therefore, designing robust deep learning models that can be deployed on resource-constrained devices has become an important research topic in the field of 6G-enabled AIoT. In this paper, we propose a method for automatically searching for robust and efficient neural network structures for AIoT systems. By introducing a skip connection structure, a feature map with reduced front-end influence can be used for calculations during the classification process. Additionally, a novel type of of dense connected search space is proposed. By relaxing this space, it is possible to search for network structures efficiently. In addition, combined with adversarial training and model delay constraints, we propose a multi-objective gradient optimization method to realize the automatic searching of network structures. Experimental results demonstrate that our method is effective for AIoT systems and superior to state-of-the-art neural architecture search algorithms.

Original languageEnglish
Pages (from-to)5332-5339
Number of pages8
JournalIEEE Internet of Things Journal
Volume8
Issue number7
Early online dateNov 2020
DOIs
Publication statusPublished - 1 Apr 2021

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