REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments

Federated Learning (FL) is a sub-domain of machine learning (ML) that enforces privacy by allowing the user's local data to reside on their device. Instead of having users send their personal data to a server where the model resides, FL flips the paradigm and brings the model to the user's device for training. Existing works share model parameters or use distillation principles to address the challenges of data heterogeneity. However, these methods ignore some of the other fundamental challenges in FL: device heterogeneity and communication efficiency. In practice, client devices in FL differ greatly in their computational power and communication resources. This is exacerbated by unbalanced data distribution, resulting in an overall increase in training times and the consumption of more bandwidth. In this work, we present a novel approach for resource-efficient FL called emph{REFT} with variable pruning and knowledge distillation techniques to address the computational and communication challenges faced by resource-constrained devices. Our variable pruning technique is designed to reduce computational overhead and increase resource utilization for clients by adapting the pruning process to their individual computational capabilities. Furthermore, to minimize bandwidth consumption and reduce the number of back-and-forth communications between the clients and the server, we leverage knowledge distillation to create an ensemble of client models and distill their collective knowledge to the server. Our experimental results on image classification tasks demonstrate the effectiveness of our approach in conducting FL in a resource-constrained environment. We achieve this by training Deep Neural Network (DNN) models while optimizing resource utilization at each client. Additionally, our method allows for minimal bandwidth consumption and a diverse range of client architectures while maintaining performance and data privacy.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Desai, Humaid Ahmed Habibullah

Chairs dc:contributor.committeechair

• Hilal, Amr E.
• Eldardiry, Hoda Mohamed

Committee member dc:contributor.committeemember

• Cho, Jin-Hee

• Federated Learning
• Variable Pruning
• Knowledge Distillation
• Resource Efficiency
• Data Privacy

Statement dc:rights

• Creative Commons Attribution-NonCommercial 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:38789

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/116686