Latency-aware Multimodal Federated Learning over UAV Networks

• URL: http://arxiv.org/abs/2510.01717v1
• Date: Thu, 02 Oct 2025 06:57:44 GMT
• Title: Latency-aware Multimodal Federated Learning over UAV Networks
• Authors: Shaba Shaon, Dinh C. Nguyen,
• Abstract summary: This paper investigates multimodal learning (FML) assisted by unmanned aerial vehicles (UAVs) with a focus on minimizing system latency.<n>In this framework, UAVs are distributed throughout the network to collect data, participate in model training, and build a global model.<n>The primary objective is to optimize FML system latency in UAV networks by jointly addressing UAV scheduling, power control, trajectory, planning, resource allocation, and BS resource management.
• Score: 5.942882029411925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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