小型ドローンにおける安全な深層学習ベースの分散知能
Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones
手のひらサイズのドローンで、霧ノードに計算をオフロードしつつ、ランダムなサブネットワークを冗長実行して霧計算を検証する分散実行スキームを提案。攻撃検出と精度向上を実現。
著者: Elia Cereda, Alessandro Giusti, Daniele Palossi
分類: cs.RO, cs.CR, cs.LG
原文アブストラクト
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.
