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

TL;DR

This paper introduces a multi-task federated learning framework for UAVs that enables concurrent training of related tasks, sharing knowledge efficiently, and optimizing communication and resource allocation for improved performance.

Contribution

It proposes a novel UAV-assisted multi-task federated learning scheme with task sharing, attention mechanisms, convergence analysis, and resource optimization strategies.

Findings

Enhanced multi-task training performance demonstrated in simulations

Improved training speed through optimized bandwidth allocation

Effective knowledge sharing across related tasks

Abstract

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.