Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation

2024-12-10

AI Summary

In UAV-assisted federated learning (FL), unreliable communication between IoT devices and the UAV leads to high training latency. Method: This paper proposes an end-to-end latency minimization framework jointly optimizing UAV trajectory, bandwidth allocation, CPU computing frequency, and transmission power. It innovatively introduces the UAV as a mobile aggregation server into the FL system and—uniquely—unifies the modeling of communication, computation, and mobility coupling. Under FL convergence constraints, the non-convex problem is decomposed, and a low-complexity alternating optimization algorithm with theoretical convergence guarantees is designed. Contribution/Results: Experiments demonstrate that the proposed method reduces training latency by up to 15.29% compared to baseline schemes, achieving training efficiency close to the ideal upper bound. This significantly enhances the practicality and deployability of UAV-assisted FL systems.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Federated learning (FL) has become a transformative paradigm for distributed machine learning across wireless networks. However, the performance of FL is often hindered by the unreliable communication links between resource-constrained Internet of Things (IoT) devices and the central server. To overcome this challenge, we propose a novel framework that employs an unmanned aerial vehicle (UAV) as a mobile server to enhance the FL training process. By capitalizing on the UAV's mobility, we establish strong line-of-sight connections with IoT devices, thereby enhancing communication reliability and capacity. To maximize training efficiency, we formulate a latency minimization problem that jointly optimizes bandwidth allocation, computing frequencies, transmit power for both the UAV and IoT devices, and the UAV's flight trajectory. Subsequently, we analyze the required rounds of the IoT devices training and the UAV aggregation for FL convergence. Based on the convergence constraint, we transform the problem into three subproblems and develop an efficient alternating optimization algorithm to solve this problem effectively. Additionally, we provide a thorough analysis of the algorithm's convergence and computational complexity. Extensive numerical results demonstrate that our proposed scheme not only surpasses existing benchmark schemes in reducing latency up to 15.29%, but also achieves training efficiency that nearly matches the ideal scenario.

Problem

• Minimize latency in UAV-enabled federated learning systems
• Optimize UAV trajectory and resource allocation for FL
• Enhance FL training efficiency via reliable UAV-IoT connections

Innovation

• UAV as mobile server enhances FL training
• Joint optimization of trajectory and resources
• Alternating optimization algorithm reduces latency