Unmanned aerial vehicles (UAVs) are emerging as mobile edge nodes for temporary coverage, aerial sensing, disaster response, public-safety monitoring, intelligent transportation, and smart-agriculture services. Although federated learning (FL) enables distributed model training without transferring raw data, conventional flat FL is not well aligned with UAV-enabled edge systems because ground clients may experience mobility-dependent availability, unstable wireless links, and costly longrange synchronization with a remote coordinator. Hierarchical federated learning (HFL) mitigates these limitations by introducing UAV edge aggregators between ground clients and the global coordinator. This article provides a simulation-based assessment of a secure HFL-UAV architecture implemented in Python. The simulator captures non-independent and identically distributed (non-IID) client data, mobility-aware client-UAV association, two-level model aggregation, malicious client behavior, trust-weighted robust aggregation, optional differential privacy, secure-aggregation overhead, communication cost, latency, and UAV energy consumption. Under a model-replacement attack, the secure HFL-UAV scheme maintains the learning performance of the considered synthetic classification task while substantially reducing the aggregation trust assigned to malicious clients. The results also expose important design tradeoffs: security mechanisms increase local communication overhead, UAV-assisted aggregation introduces energy cost, and trust filtering must be calibrated carefully under non-IID data. The proposed simulation framework therefore provides a reproducible basis for evaluating secure aerial edge learning systems.
Ton That Tam Dinh, Manh Cuong Ho, Ayalneh Bitew Wondmagegn et al.· International Conference on...· 0 citations
Open Radio Access Network (O-RAN) enables flexible and intelligent radio access network operation through disaggregation, virtualization, open interfaces, and RAN Intelligent Controllers (RICs). At the same time, the data required to train artificial intelligence and machine learning models in O-RAN is naturally distributed across user equipment, base stations, edge clouds, and management entities, which makes centralized learning costly and privacy-sensitive. Federated Learning (FL) has therefore emerged as a promising paradigm for O-RAN intelligence because it enables distributed model training without transferring raw data. In this work, we survey recent studies on the fusion of FL and O-RAN and classify them into three categories: 1) FL-assisted network control, where FL is used as a collaborative learning tool for slicing, offloading, routing, and security; 2) FL training-efficiency optimization, where communication cost, learning latency, resource consumption, and convergence are improved under O-RAN constraints; and 3) integrated approaches that jointly consider network performance and FL efficiency. Based on this taxonomy, we discuss open research challenges, including device heterogeneity, mobility, RIC integration, communication-efficient learning, and security threats, such as model poisoning and inference attacks.
Junsuk Oh, Donghyun Lee, Chunghyun Lee et al.· International Conference on...· 0 citations
Intent-Based Networking (IBN) has emerged as a promising paradigm for simplifying network management by allowing operators and applications to specify high-level service objectives rather than low-level device configurations. Early IBN research was mainly developed in Software-Defined Networking (SDN), Network Function Virtualization (NFV), transport networks, core networks, and data-center environments, where programmability, virtualization, and relatively stable infrastructure models enabled intent translation, orchestration, and assurance. However, realizing IBN in end-to-end mobile networks is more challenging because the Radio Access Network (RAN) is highly dynamic, wireless-channeldependent, mobility-sensitive, and governed by multiple control timescales. The emergence of Open RAN (O-RAN) changes this landscape by making the RAN programmable, disaggregated, data-driven, and control-lable through non-real-time and near-real-time intelligent control loops. This survey reviews the evolution of IBN from SDN/NFV-enabled automation toward O-RAN-driven end-to-end intent-based networking for 5G-Advanced and 6G. We discuss architectural mechanisms, key challenges, recent advances in AI-driven and agentic IBN, and future research directions including Large Language Model (LLM)-based intent translation, contractbased O-RAN slicing, digital twin-assisted validation, and trustworthy closed-loop orchestration.
Dongwook Won, Thanh Thien-An Dang, Ton That Tam Dinh et al.· International Conference on...· 0 citations