# Collaborative Learning with a Drone Orchestrator

arXiv: https://arxiv.org/abs/2303.02266 · HTML: https://mlsanity.com/paper/2303.02266/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach](https://mlsanity.com/paper/2004.03877/) (2020)
- [Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2102.03401/) (2021)
- [UAV Communications for Sustainable Federated Learning](https://mlsanity.com/paper/2103.11073/) (2021)
- [Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2209.06623/) (2022)
- [The Sky Is Not the Limit: LTE for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1707.07534/) (2017)
- [Green Tethered UAVs for EMF-Aware Cellular Networks](https://mlsanity.com/paper/2105.14359/) (2021)

## Built on by (most cited first, 3 of 3)

- [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025)
- [Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation](https://mlsanity.com/paper/2412.07428/) (2024)
- [MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation](https://mlsanity.com/paper/2602.18332/) (2026)

## Abstract (arXiv)

In this paper, the problem of drone-assisted collaborative learning is considered. In this scenario, swarm of intelligent wireless devices train a shared neural network (NN) model with the help of a drone. Using its sensors, each device records samples from its environment to gather a local dataset for training. The training data is severely heterogeneous as various devices have different amount of data and sensor noise level. The intelligent devices iteratively train the NN on their local datasets and exchange the model parameters with the drone for aggregation. For this system, the convergence rate of collaborative learning is derived while considering data heterogeneity, sensor noise levels, and communication errors, then, the drone trajectory that maximizes the final accuracy of the trained NN is obtained. The proposed trajectory optimization approach is aware of both the devices data characteristics (i.e., local dataset size and noise level) and their wireless channel conditions, and significantly improves the convergence rate and final accuracy in comparison with baselines that only consider data characteristics or channel conditions. Compared to state-of-the-art baselines, the proposed approach achieves an average 3.85% and 3.54% improvement in the final accuracy of the trained NN on benchmark datasets for image recognition and semantic segmentation tasks, respectively. Moreover, the proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time, communication overhead, and battery usage, respectively for these tasks.