Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things

Nai Wang, Junjun Chen, Di Wu, Wencheng Yang, Yong Xiang, Atul Sajjanhar

Abstract: The Internet of Things (IoT) concept has increasingly gained popularity among different industrial and scientific fields by leveraging the development of modern electronic chip manufacturing and networking hardware and techniques. As the combination of end devices and networking techniques, it becomes more and more possible that people can organize an IoT system to serve more extensive industrial and scientific needs by running a higher level of machine learning models, such as Federated Learning (FL) and Knowledge Distillation (KD) architectures. However, security over network communications is vital when the whole system spreads to massive scales in networking, including data integrity and robustness over external malicious attacks, which would significantly affect the system’s overall modal training effectiveness. In this paper, we propose an ungraded version of the Non-Fungible Token (NFT) assisted Knowledge Distillation framework, aiming at leveraging the blockchain features on data security to solve the intrinsic robustness defects in a naive KD architecture and reducing overall processing time by adding a local blockchain layer. Our major contributions can be concluded as the following (1) a revised version Hybrid KD-NFT framework from our previously proposed State-of-the-art (SOTA) framework, which improves the overall effectiveness and reduces the overall latency when compared to its original version; (2) the proposal and implementation of a hybrid (combination of private and public chain) multi-layer blockchain architecture; and (3) conducting more sets of experiments and performing more comprehensive comparisons over existing other KD frameworks in more metrics to prove our proposed Hybrid KD-NFT framework is a valid work.