Adaptive Federated Learning for Future IoV-Oriented IoT End-to-End Network Planning

Author First name, Last name, Institution

Jiaming Pei, School of Computer Science
Xiaoqing Xu, Shandong University of Science and Technology
Lukun Wang, Shandong University of Science and Technology
Saba Al-Rubaye, Cranfield University
Sun Zhang, Anhui University of Finance and Economics
Anwer Al-Dulaimi, Zayed University

Document Type

Article

Source of Publication

IEEE Internet of Things Journal

Publication Date

1-1-2026

Abstract

In the Internet of Things (IoT) domain, end-to-end (E2E) planning tasks require distributed devices to collaboratively train deep models under highly dynamic environments. However, existing federated learning (FL) methods often assume homogeneous communication conditions and static node reliability, leading to suboptimal aggregation performance when confronted with heterogeneous uncertainty sources such as sensing noise, prediction bias, and communication instability. To address this challenge, we propose FedUAP (Federated Uncertainty-Aware End-to-End Planning), a novel framework that dynamically adjusts client contributions based on multi-source uncertainty and network topology information. Specifically, each IoV vehicle node within the broader IoT system estimates three uncertainty factors—prediction uncertainty, sensing uncertainty, and communication uncertainty—to represent its model reliability and transmission stability. A topology-aware weighting module further refines the aggregation by incorporating node connectivity and link quality. In addition, a temporal smoothing strategy is introduced to stabilize weight evolution over successive communication rounds. Extensive experiments on various E2E IoV-centric IoT planning scenarios demonstrate that FedUAP achieves superior convergence stability, communication efficiency, and planning accuracy compared with existing adaptive aggregation and uncertainty-based FL baselines. The proposed approach provides a promising direction toward uncertainty-robust and topology-adaptive federated optimization in large-scale IoT and IoV networks.

DOI Link

10.1109/jiot.2026.3670827

ISSN

2327-4662

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Disciplines

Computer Sciences

Keywords

End-to-end planning, federated learning, uncertainty-aware aggregation

Scopus ID

105032132800

Recommended Citation

Pei, Jiaming; Xu, Xiaoqing; Wang, Lukun; Al-Rubaye, Saba; Zhang, Sun; and Al-Dulaimi, Anwer, "Adaptive Federated Learning for Future IoV-Oriented IoT End-to-End Network Planning" (2026). All Works. 7876.
https://zuscholars.zu.ac.ae/works/7876

Indexed in Scopus

yes

Open Access

no