Secure Deep Learning-based Distributed Intelligence on Pocket-sized
  Drones
Elia Cereda Alessandro Giusti Daniele Palossi
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.