Self-evolving networks (SENs) are emerging technologies that dynamically and autonomously adapt and optimize their performance and behaviour based on changing conditions and evolving requirements. With the advent of fifth-generation (5G) wireless technologies and the resurgence of machine learning, SENs are expected to become a critical component of future wireless networks. In particular, integrated vertical heterogeneous network (VHetNet) architectures, which enable dynamic, three-dimensional (3D), and agile topologies, are likely to form a key foundation for SENs. However, the distributed multi-level computational and communication structure and the fully dynamic nature of self-evolving integrated VHetNets (SEI-VHetNets) necessitate the deployment of an enhanced distributed learning and computing mechanism to enable full integration and coordination. To address this need, we propose a novel learning technique, multi-tier hierarchical federated learning (MT-HFL), based on hierarchical federated learning (HFL) that enables full integration and coordination across vertical tiers. Through MT-HFL, SEI-VHetNets can learn and adapt to dynamic network conditions, optimize resource allocation, and enhance user experience in a real-time, scalable, and accurate manner while preserving user privacy. This paper presents the key characteristics and challenges of SEI-VHetNets and discusses how MT-HFL addresses them. We also discuss potential use cases and present a case study demonstrating the advantages of MT-HFL over conventional terrestrial HFL approaches.

翻译：自演进网络（SENs）是一种新兴技术，能够根据动态变化的条件和不断演进的需求，自主、自适应地优化其性能与行为。随着第五代（5G）无线技术的到来以及机器学习技术的复兴，SENs有望成为未来无线网络的关键组成部分。特别是，集成垂直异构网络（VHetNet）架构能够实现动态、三维（3D）且灵活的拓扑结构，很可能成为SENs的核心基础。然而，自演进集成垂直异构网络（SEI-VHetNets）的分布式多层计算与通信结构及其完全动态的特性，要求部署增强型分布式学习与计算机制以实现全面集成与协调。为应对这一需求，我们提出了一种新颖的学习技术——多层分层联邦学习（MT-HFL），该技术基于分层联邦学习（HFL），能够实现垂直各层之间的全面集成与协调。通过MT-HFL，SEI-VHetNets能够学习并适应动态网络条件，优化资源分配，以实时、可扩展且精确的方式提升用户体验，同时保障用户隐私。本文阐述了SEI-VHetNets的关键特征与挑战，并讨论了MT-HFL如何应对这些挑战。我们还将探讨潜在的应用场景，并通过案例研究展示MT-HFL相较于传统地面HFL方法的优势。