Proof of Concept for FedRelax on Kubernetes: An Implementation Guide
Loading...
URL
Journal Title
Journal ISSN
Volume Title
School of Electrical Engineering |
Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Authors
Date
2024-11-27
Department
Major/Subject
Electronic and Digital Systems
Mcode
Degree programme
Master's Programme in Automation and Electrical Engineering
Language
en
Pages
107
Series
Abstract
In recent years, federated learning has been introduced and gained interest in different research areas. The evolution of federated learning has enabled an ability to train models on distributed datasets residing on multiple devices while preserving data privacy. FedRelax, a novel federated learning approach, is designed to train a heterogeneous network of models. Kubernetes is the most popular container orchestration tool to handle distributed environment and facilitate the application deployment, scaling and management. This thesis aims to study the integration of cloud technologies and federated learning system to investigate the behavior of leveraging these technologies together in real-world scenarios. Specifically, the study explores the feasibility of implementing the FedRelax algorithm, an novel federated learning method, on a Kubernetes cluster. The thesis study includes the design and implementation documentation for a Proof of Concept (POC). The performance, scalability and fault tolerance of the POC will be evaluated through different experiments and test scenarios. A baseline approach that runs FedRelax locally will be used as a benchmark to analyze the performance of this POC. When comparing the POC with baseline approach, a model generated by POC has less accuracy and higher runtime due to the complexity of distributed environment. However, the implementation enable a reliable scaling and fault tolerance mechanisms. Even though there are still potential areas to improve, the results shows that it is feasible to implement FedRelax algorithm on Kubernetes environment and the algorithm can perform training in a distributed settings.Description
Supervisor
Jung, AlexThesis advisor
Sarcheshmehpour, YasminKeywords
federated learning, cloud native technologies, FedRelax, docker, kubernetes, software development