# Dynamic Coded Distributed Convolution for UAV-based Networked Airborne Computing

arXiv: https://arxiv.org/abs/2201.01431 · HTML: https://mlsanity.com/paper/2201.01431/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2022.

## Built on by (most cited first, 2 of 2)

- [Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler Mitigation](https://mlsanity.com/paper/2202.09019/) (2022)
- [Flexible Coded Distributed Convolution Computing for Enhanced Straggler Resilience and Numerical Stability in Distributed CNNs](https://mlsanity.com/paper/2411.01579/) (2024)

## Abstract (arXiv)

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.