Self-Evolving Integrated VHetNets for 6G: A Multi-Tier HFL Approach

by   Amin Farajzadeh, et al.

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.


page 1

page 2

page 6


Wireless Federated Learning (WFL) for 6G Networks – Part I: Research Challenges and Future Trends

Conventional machine learning techniques are conducted in a centralized ...

Self-Evolving Integrated Vertical Heterogeneous Networks

The evolution of future networks into fully autonomous entities appears ...

Machine Learning for Metaverse-enabled Wireless Systems: Vision, Requirements, and Challenges

Today's wireless systems are posing key challenges in terms of quality o...

A Vision of Self-Evolving Network Management for Future Intelligent Vertical HetNet

Future integrated terrestrial-aerial-satellite networks will have to exh...

Multi-Stage Hybrid Federated Learning over Large-Scale Wireless Fog Networks

One of the popular methods for distributed machine learning (ML) is fede...

STAR-RIS Assisted Over-the-Air Vertical Federated Learning in Multi-Cell Wireless Networks

Vertical federated learning (FL) is a critical enabler for distributed a...

The Computing of Digital Ecosystems

A primary motivation for our research in digital ecosystems is the desir...

Please sign up or login with your details

Forgot password? Click here to reset