复旦大学数字信号处理与传输实验室的杨宇博同学的论文Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing，被IEEE Internet of Things Journal录用

论文摘要：Unmanned aerial vehicle (UAV) swarms are extensively used in emergency communications, area monitoring, and disaster relief. Their operations are coordinated by control centers, making them well-suited for federated learning (FL) frameworks. However, current UAV FL methods ignore the rich information contained in UAV images and the potential of using a single dataset to accomplish multiple tasks. For instance, in disaster relief scenarios, images acquired by UAVs can support tasks like crowd detection, road passability analysis, and disaster impact assessment. These tasks exhibit time-varying demands and may have potential correlations. To meet these requirements, this paper introduces two core mechanisms: a dynamic task attention mechanism to evaluate task importance for efficient resource allocation, and a task affinity (TA) metric to capture inter-task correlations for knowledge sharing. Building on these innovations, we propose FedDya, a novel UAV swarm-based one-dataset multi-task FL framework, where ground emergency vehicles (EVs) collaborate with UAVs to accomplish multiple tasks leveraging a single dataset. To optimize resource allocation, we formulate a two-layer optimization problem to jointly optimize UAV transmission power, computation frequency, bandwidth allocation, and UAV-EV associations. For the inner problem, we derive closed-form solutions for transmission power, computation frequency, and bandwidth allocation and apply the block coordinate descent method for optimization. For the outer problem, a novel two-stage algorithm is designed to determine optimal UAV-EV associations. Furthermore, theoretical analysis reveals a trade-off between UAV energy consumption violation and multi-task performance, characterized by an O( √ V , 1/V ) relationship. Extensive simulation results further validate the effectiveness of the proposed scheme.

论文作者：Yubo Yang, Tao Yang, Xiaofeng Wu, Ziyu Guo, Bo Hu