发表机构
University of Kansas（堪萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对物联网无人机联邦学习的传输干扰问题，提出公平共识双层优化算法，结合阈值与功率控制提升数据包交付率，性能优于基线策略。
AI 中文摘要
无人机（UAV）赋能的联邦学习（FL）可为大规模物联网（IoT）部署提供灵活、按需的边缘智能，但工作在共享非授权频段会导致上行链路更新传输存在干扰耦合且不可靠。本文开发了一种数据包级传输框架，该框架捕获缓冲区溢出、延迟违规和传输错误，并利用得到的数据包交付率（PDR）表示通过伯努利掩码分组的FL聚合过程实现的部分更新接收。随后，我们提出一种公平共识双层（FCB）优化，该优化联合控制两项内容：一是传输阈值，以在部分可观测性下达成共识的同时最大化平均PDR；二是传输功率，以提升最差PDR并确保物联网学习者之间的公平性。为解决该问题，我们提出一种交替FCB优化器，由基于共识的阈值控制器（CTC）和基于公平性的功率控制器（FPC）组成，其中CTC驱动物联网学习者在传输阈值上达成PDR高效共识，FPC则在所得共识阈值下更新传输功率以提升最差PDR并确保公平性。基于卷积神经网络（CNN）的FL任务数值结果表明，FCB优化器通过增强数据包级更新交付提升了FL聚合与训练性能，始终优于基线传输策略。
英文摘要
Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmission framework that captures buffer overflow, delay violations, and transmission errors, and uses the resulting packet delivery ratio (PDR) to represent partial-update reception through a packetized, Bernoulli-masked FL aggregation process. We then formulate a fairness-consensus bilevel (FCB) optimization that jointly controls (i) transmission thresholds to maximize the average PDR while reaching consensus under partial observability and (ii) transmission powers to improve the worst PDR and enforce fairness across IoT learners. To solve this problem, we propose an alternating FCB optimizer composed of a consensus-based threshold controller (CTC), which drives the IoT learners toward a PDR-efficient consensus on transmission thresholds, and a fairness-based power controller (FPC), which updates transmission powers to improve the worst PDR and ensure fairness under the resulting consensus thresholds. Numerical results on CNN-based FL tasks show that the FCB optimizer improves FL aggregation and training performance by enhancing packet-level update delivery, consistently outperforming baseline transmission policies.
CommentsGLOBECOM 2026-2026 IEEE Global Communications Conference
