Multi-blockchain framework for scalable and communication-efficient federated edge learning
This project is an implementation of a proposed scalable and communication-efficient federated edge learning framework by using multiple blockchains. The report is only a part of the proposed framework, mainly the multiple blockchain and communication system to allow different blockchains to communi...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/148003 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-148003 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1480032021-04-30T03:16:32Z Multi-blockchain framework for scalable and communication-efficient federated edge learning Lai, Yan Ting Dusit Niyato School of Computer Science and Engineering DNIYATO@ntu.edu.sg Engineering::Computer science and engineering::Information systems::Information systems applications This project is an implementation of a proposed scalable and communication-efficient federated edge learning framework by using multiple blockchains. The report is only a part of the proposed framework, mainly the multiple blockchain and communication system to allow different blockchains to communicate with each other. It also studies the advantages and limitations of the proposed tool, WeCross Platform, to implement the communication system and checks if it is suitable to be used for the proposed framework. Bachelor of Engineering (Computer Engineering) 2021-04-30T03:16:32Z 2021-04-30T03:16:32Z 2021 Final Year Project (FYP) Lai, Y. T. (2021). Multi-blockchain framework for scalable and communication-efficient federated edge learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148003 https://hdl.handle.net/10356/148003 en application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Computer science and engineering::Information systems::Information systems applications |
spellingShingle |
Engineering::Computer science and engineering::Information systems::Information systems applications Lai, Yan Ting Multi-blockchain framework for scalable and communication-efficient federated edge learning |
description |
This project is an implementation of a proposed scalable and communication-efficient federated edge learning framework by using multiple blockchains. The report is only a part of the proposed framework, mainly the multiple blockchain and communication system to allow different blockchains to communicate with each other. It also studies the advantages and limitations of the proposed tool,
WeCross Platform, to implement the communication system and checks if it is suitable to be used for the proposed framework. |
author2 |
Dusit Niyato |
author_facet |
Dusit Niyato Lai, Yan Ting |
format |
Final Year Project |
author |
Lai, Yan Ting |
author_sort |
Lai, Yan Ting |
title |
Multi-blockchain framework for scalable and communication-efficient federated edge learning |
title_short |
Multi-blockchain framework for scalable and communication-efficient federated edge learning |
title_full |
Multi-blockchain framework for scalable and communication-efficient federated edge learning |
title_fullStr |
Multi-blockchain framework for scalable and communication-efficient federated edge learning |
title_full_unstemmed |
Multi-blockchain framework for scalable and communication-efficient federated edge learning |
title_sort |
multi-blockchain framework for scalable and communication-efficient federated edge learning |
publisher |
Nanyang Technological University |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/148003 |
_version_ |
1698713654706307072 |