Скачать или смотреть Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones

This video is part of the supplementary material of the paper "Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones," EWSN, 2023.

[Abstract] Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. To tackle this concern, we propose a novel distributed edge-fog execution scheme that validates fog computation by redundantly executing a random subnetwork aboard our nano-drone. Compared to a State-of-the-Art visual pose estimation network that entirely runs onboard, a larger network executed in a distributed way improves the R2 score by +0.19; in case of attack, our approach detects it within 2 s with 95% probability.

Reference:
E. Cereda, A. Giusti, and D. Palossi, "Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones," In Proceedings of the 2023 International Conference on Embedded Wireless Systems and Networks (EWSN), ACM, 2023.