  A single unmanned aerial vehicle (UAV) has limited computing resources and
battery capacity, making it difficult to handle computationally intensive tasks
such as the convolution operations in many deep learning applications.
UAV-based networked airborne computing (NAC) is a promising technique to
address this challenge. It allows UAVs within a range to share resources among
each other via UAV-to-UAV communication links and carry out
computation-intensive tasks in a collaborative manner. This paper investigates
the vector convolution problem over the NAC architecture. A novel dynamic coded
convolution strategy with privacy awareness is developed to address the unique
features of UAV-based NAC, including node heterogeneity, frequently changing
network typologies, time-varying communication and computation resources.
Simulation results show its high efficiency and resilience to uncertain
stragglers.
