LSTM based content prediction for edge caching using federated learning approach

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Détails bibliographiques
Auteurs principaux:	Mazumder, Shafkat Ahmed, Paul, Piash, ZUBAIR, DIN MOHAMMAD, Haque, Maksudul, Mayukh, Jidni
Autres auteurs:	Alam, Md. Golam Rabiul
Format:	Thèse
Langue:	English
Publié:	Brac University 2021
Sujets:	Federated Learning Edge Computing Edge Caching Content Prediction Long Short Term Memory Decentralized Learning System Cache-Hit Ratio Edge computing.
Accès en ligne:	http://hdl.handle.net/10361/15208

id	10361-15208
record_format	dspace
spelling	10361-152082022-01-26T10:21:52Z LSTM based content prediction for edge caching using federated learning approach Mazumder, Shafkat Ahmed Paul, Piash ZUBAIR, DIN MOHAMMAD Haque, Maksudul Mayukh, Jidni Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Federated Learning Edge Computing Edge Caching Content Prediction Long Short Term Memory Decentralized Learning System Cache-Hit Ratio Edge computing. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (page 29-31). With rapid expansion and worldwide penetration of internet usage, there has been a rapid growth and development in the field of communication technology. To meet a never ending demand of excellence in quality and computation, a relatively new and effective computation theory called Edge computing is making its mark. Edge computing basically means the computing which is done at or near the data source instead of relying on the cloud to do all the work which enhances network performance by reducing latency. With Edge computing and Edge caching we seek to integrate federated learning approach by training the model across multiple edge nodes that have thier own local environment, without exchanging them which will eventually turn into Edge Intelligence by increasing system level optimization making content delivery faster than before. In a whole in this research topic we aim to investigate service provisioning in edge computing which will make our daily used devices more efficient in terms of performance and keep our personal data secured with the help of federated learning approach. Accurate content prediction combined with optimized caching promises to be a future-proof solution. We adopt a hierarchy based three layer system architecture in which we integrate federated learning with LSTM for predicting content based on view count. With our FedPredict algorithm we intend to maximize cache hit so that the network flow remains optimized. Lastly, we look into potential optimization our algorithm and address some areas of improvement regarding distributed learning systems. Shafkat Ahmed Mazumder Piash Pau DIN MOHAMMAD ZUBAIR Maksudul Haque Jidni Mayukh B. Computer Science 2021-10-11T06:35:59Z 2021-10-11T06:35:59Z 2021 2021-06 Thesis ID 17101093 ID 17101040 ID 17101168 ID 17101084 ID 17101139 http://hdl.handle.net/10361/15208 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 31 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Federated Learning Edge Computing Edge Caching Content Prediction Long Short Term Memory Decentralized Learning System Cache-Hit Ratio Edge computing.
spellingShingle	Federated Learning Edge Computing Edge Caching Content Prediction Long Short Term Memory Decentralized Learning System Cache-Hit Ratio Edge computing. Mazumder, Shafkat Ahmed Paul, Piash ZUBAIR, DIN MOHAMMAD Haque, Maksudul Mayukh, Jidni LSTM based content prediction for edge caching using federated learning approach
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Mazumder, Shafkat Ahmed Paul, Piash ZUBAIR, DIN MOHAMMAD Haque, Maksudul Mayukh, Jidni
format	Thesis
author	Mazumder, Shafkat Ahmed Paul, Piash ZUBAIR, DIN MOHAMMAD Haque, Maksudul Mayukh, Jidni
author_sort	Mazumder, Shafkat Ahmed
title	LSTM based content prediction for edge caching using federated learning approach
title_short	LSTM based content prediction for edge caching using federated learning approach
title_full	LSTM based content prediction for edge caching using federated learning approach
title_fullStr	LSTM based content prediction for edge caching using federated learning approach
title_full_unstemmed	LSTM based content prediction for edge caching using federated learning approach
title_sort	lstm based content prediction for edge caching using federated learning approach
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15208
work_keys_str_mv	AT mazumdershafkatahmed lstmbasedcontentpredictionforedgecachingusingfederatedlearningapproach AT paulpiash lstmbasedcontentpredictionforedgecachingusingfederatedlearningapproach AT zubairdinmohammad lstmbasedcontentpredictionforedgecachingusingfederatedlearningapproach AT haquemaksudul lstmbasedcontentpredictionforedgecachingusingfederatedlearningapproach AT mayukhjidni lstmbasedcontentpredictionforedgecachingusingfederatedlearningapproach
_version_	1814309568848592896

LSTM based content prediction for edge caching using federated learning approach

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