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arXiv · 2307.01559

Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones

Elia Cereda·Alessandro Giusti·Daniele Palossi
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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 $R^2$ score by +0.19; in case of attack, our approach detects it within 2s with 95% probability.

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Elia Cereda, Alessandro Giusti, Daniele Palossi. 2023-07-04. Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones. https://arxiv.org/abs/2307.01559

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