Resource Consumption Analysis of Distributed Machine Learning Models for 6G Security - Master of Science (Tech) Thesis

Aug 2, 2026

Uploaded by Noah

Machine learning is increasingly essential for network security as communications networks grow more complex and data volumes expand across access, backhaul, and core domains. This thesis investigates the resource consumption implications of deploying machine learning techniques for 6G security by identifying key resources and the enablers of resource efficiency. It studies distributed learning—Federated Learning and Split Learning—through experimental and comparative analysis covering computing, memory, bandwidth, energy, latency, and human resources, emphasizing sustainability-aware deployment trade-offs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages98

Reading Time4 h 7 min

AccessFree to download

Introduction

Problem Statement

Research Questions

Research Objectives

Thesis Organization

Literature Review

Introduction to 5G and the Need for 6G Network

Security Challenges in 5G and 6G

Machine Learning

Centralized and Distributed ML

Applications of ML in Communication Networks

Applications of ML in Network Security

ML for Security in 6G

Resource Consumption Analysis of Distributed Machine Learning Models for 6G Security University of Turku Faculty of Technology Master of Science (Tech) Thesis Information and Communication Technology (Cyber Security) May 2024 Md Muzammal Hoque Supervisors: Mohammad Tahir (University of Turku) Petri Sainio (University of Turku) Ijaz Ahmad (VTT Technical Research Centre of Finland) The originality of this thesis has been checked in accordance with the University of Turku quality assurance system using the Turnitin OriginalityCheck service. UNIVERSITY OF TURKU Faculty of Technology Md Muzammal Hoque: Resource Consumption Analysis of Distributed Machine Learning Models for 6G Security Master of Science (Tech) Thesis, 66 p. Information and Communication Technology (Cyber Security) May 2024 Communications networks have become increasingly complex environments due to the massive increase in the number of communicating nodes and diverse services with unique requirements. Therefore, machine learning has become extremely important from the access to backhaul and core networks, as well as various technologies required for the smooth operations of different tasks and services within those networks. The overall complexity of networked environments and increasing volumes of data further complicates the network security landscape. Machine learning with its various techniques and tools, thus, has become vital for network security. In 6G network security, the promises of machine learning are vast, from preventive measures to detection to response and remediation. However, machine learning requires a huge amount of resources mainly due to the fact that machine learning operates on data and data volumes are consistently rising. This work studied and investigated the resource consumption of machine learning techniques used for network security to provide insights into the potential resource implications of deploying machine learning in 6G security. The thesis explored a wide range of state-of-the-art resource-efficient Machine learning based security solutions to find out the key resources consumed by those solutionsand the key enablers of resource efficiency for those solutions. In particular, the thesis focused on investigating the resource consumption of distributed learning for 6G networks in terms of computing, memory, bandwidth, energy, latency, and human resources. Distributed machine learning is highly relevant to the context of 6G, as it can meet the future 6G requirement of processing substantial amounts of data generated from numerous devices while preserving data privacy and security. The thesis presents an experimental and comparative analysis of the Federated Learning (FL) and Split Learning (SL) based network security solutions, which are the two most popular distributed learning in terms of resource consumption fingerprinting. The finding shows that both models perform well, while Federated Learning appears to have a slight edge over Split Learning in terms of precision and F1 score. However, the differences are quite small. In terms of resource consumption fingerprinting, we observed that both of them have their advantages and shortcomings. In terms of CPU usage, SL had higher CPU usage, while FL had higher peaks and variability. In terms of memory usage, FL was more memory efficient than the SL. Finally, SL was more time and power-efficient and had lower CO2 emission. Keywords: Security; 6G; Machine Learning; Network Security; distributed learning; 6G security; sustainability; DNN;Split Learning; Federated Learning; Resource Consumption; Resource Efficiency Acknowledgements I want to express my deep gratitude to my supervisor at the VTT Technical Research Centre of Finland, Ijaz Ahmad (PhD.), and my supervisors from the University of Turku, Tahir Mohammad (PhD.) and Petri Sainio. Their priceless advice, continuous assistance, and constructive feedback have been crucial in enhancing my compre

FAQs

Why does the thesis focus on resource consumption for 6G network security?

Machine learning for security depends on data, and data volumes keep increasing. The thesis studies how deploying machine learning affects key resource usage in 6G security scenarios.

Which distributed learning approaches are compared in this thesis?

The thesis presents an experimental and comparative analysis of Federated Learning (FL) and Split Learning (SL) network security solutions.

What are the main differences in resource usage between Federated Learning and Split Learning?

SL shows higher CPU usage, while FL has higher peaks and variability. FL is more memory efficient, while SL is more time and power-efficient with lower CO2 emission.

Aug 2, 2026

Uploaded by Noah