Latency-aware Multimodal Federated Learning over UAV Networks
This work addresses latency optimization for UAV-assisted multimodal federated learning, which is an incremental improvement in a domain-specific application.
The paper tackles the problem of minimizing system latency in federated multimodal learning (FML) assisted by unmanned aerial vehicles (UAVs) by jointly optimizing sensing scheduling, power control, trajectory planning, and resource allocation, resulting in outperformance over existing approaches in latency and model training performance as shown in numerical experiments.
This paper investigates federated multimodal learning (FML) assisted by unmanned aerial vehicles (UAVs) with a focus on minimizing system latency and providing convergence analysis. In this framework, UAVs are distributed throughout the network to collect data, participate in model training, and collaborate with a base station (BS) to build a global model. By utilizing multimodal sensing, the UAVs overcome the limitations of unimodal systems, enhancing model accuracy, generalization, and offering a more comprehensive understanding of the environment. The primary objective is to optimize FML system latency in UAV networks by jointly addressing UAV sensing scheduling, power control, trajectory planning, resource allocation, and BS resource management. To address the computational complexity of our latency minimization problem, we propose an efficient iterative optimization algorithm combining block coordinate descent and successive convex approximation techniques, which provides high-quality approximate solutions. We also present a theoretical convergence analysis for the UAV-assisted FML framework under a non-convex loss function. Numerical experiments demonstrate that our FML framework outperforms existing approaches in terms of system latency and model training performance under different data settings.