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Introduction

With the rapid growth of Internet of Things (IoT) devices and the increasing demand for real-time data processing, edge computing has emerged as a promising solution to address the limitations of cloud computing. Edge computing refers to the practice of processing data closer to its source, thereby reducing latency and conserving bandwidth. However, optimizing edge computing resource allocation and management presents significant challenges due to the dynamic and decentralized nature of edge networks.

Reinforcement learning (RL) has shown great potential in optimizing resource allocation and management in various domains. By leveraging RL techniques, edge computing systems can adaptively allocate computing resources based on changing workloads and environmental conditions. However, traditional RL algorithms often require centralized data collection and model training, which may not be feasible in edge computing environments due to privacy and scalability concerns.

Federated reinforcement learning (FRL) offers a promising solution by enabling distributed edge devices to collaboratively learn a shared policy without sharing raw data. FRL leverages edge devices’ local data for model training while aggregating global updates to improve overall performance. This thesis aims to investigate the use of FRL for optimizing edge computing resource allocation and management.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Edge Computing
2.2 Reinforcement Learning
2.3 Federated Learning
2.4 Resource Allocation in Edge Computing
2.5 Optimization Techniques in Edge Computing
2.6 Federated Reinforcement Learning
2.7 Applications of FRL in Edge Computing
2.8 Challenges in FRL for Edge Computing
2.9 Existing Solutions and Approaches
2.10 Gaps in Literature

Chapter 3: Research Methodology
3.1 Research Approach
3.2 Data Collection
3.3 Model Design
3.4 Experiment Setup
3.5 Evaluation Metrics
3.6 Performance Evaluation
3.7 Ethical Considerations
3.8 Data Security and Privacy

Chapter 4: Discussion of Findings
4.1 Data Analysis
4.2 Model Performance
4.3 Comparison with Baseline Algorithms
4.4 Scalability and Efficiency
4.5 Robustness and Adaptability
4.6 Implications for Edge Computing
4.7 Future Research Directions
4.8 Recommendations for Industry

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Knowledge
5.3 Practical Implications
5.4 Limitations and Future Work
5.5 Conclusion

Thesis Overview

Federated reinforcement learning (FRL) has emerged as a promising approach for optimizing resource allocation and management in edge computing environments. This thesis investigates the use of FRL to improve the efficiency and performance of edge computing systems. The literature review explores the current state of research in edge computing, reinforcement learning, federated learning, and their applications in resource allocation optimization. The research methodology outlines the approach taken to design, implement, and evaluate FRL models for edge computing optimization. The discussion of findings presents the results of experiments and analyzes the performance of FRL in comparison with baseline algorithms. The conclusion and summary section summarizes the key findings, contributions to knowledge, and future research directions in FRL for edge computing optimization. This thesis aims to provide valuable insights and recommendations for researchers and practitioners in the field of edge computing and machine learning.

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