# UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing

arXiv: https://arxiv.org/abs/2501.10644 · HTML: https://mlsanity.com/paper/2501.10644/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning](https://mlsanity.com/paper/1911.12423/) (2019)
- [Efficient Device Scheduling with Multi-Job Federated Learning](https://mlsanity.com/paper/2112.05928/) (2021)
- [Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management](https://mlsanity.com/paper/2011.14197/) (2020)
- [Multi-Job Intelligent Scheduling with Cross-Device Federated Learning](https://mlsanity.com/paper/2211.13430/) (2022)
- [Federated Learning via Unmanned Aerial Vehicle](https://mlsanity.com/paper/2210.10970/) (2022)

## Built on by (most cited first, 1 of 1)

- [Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing](https://mlsanity.com/paper/2503.09144/) (2025)

## Abstract (arXiv)

The rapid development of Unmanned aerial vehicles (UAVs) technology has spawned a wide variety of applications, such as emergency communications, regional surveillance, and disaster relief. Due to their limited battery capacity and processing power, multiple UAVs are often required for complex tasks. In such cases, a control center is crucial for coordinating their activities, which fits well with the federated learning (FL) framework. However, conventional FL approaches often focus on a single task, ignoring the potential of training multiple related tasks simultaneously. In this paper, we propose a UAV-assisted multi-task federated learning scheme, in which data collected by multiple UAVs can be used to train multiple related tasks concurrently. The scheme facilitates the training process by sharing feature extractors across related tasks and introduces a task attention mechanism to balance task performance and encourage knowledge sharing. To provide an analytical description of training performance, the convergence analysis of the proposed scheme is performed. Additionally, the optimal bandwidth allocation for UAVs under limited bandwidth conditions is derived to minimize communication time. Meanwhile, a UAV-EV association strategy based on coalition formation game is proposed. Simulation results validate the effectiveness of the proposed scheme in enhancing multi-task performance and training speed.