Heterogeneous Workload-Based Consumer Resource Recommendation Model for Smart Cities: eHealth Edge–Cloud Connectivity Using Federated Split Learning-Reference-Cited by-同舟云学术

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Heterogeneous Workload-Based Consumer Resource Recommendation Model for Smart Cities: eHealth Edge–Cloud Connectivity Using Federated Split Learning

Published:2024-02 Issue:1 Volume:70 Page:4187-4196
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Ahmed Syed Thouheed¹^ORCID,Kumar V. Vinoth²,Jeong Junho³^ORCID

Affiliation:

1. Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India

2. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India

3. Department of Computer Science and Engineering, Dongguk University, Seoul, Republic of Korea

Funder

Dongguk University Research Fund of 2023

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/30/10510045/10473185.pdf?arnumber=10473185

Reference19 articles.

1. A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

2. A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields

3. A Review of Content-Based and Context-Based Recommendation Systems

4. Federated Recommendation Systems

5. A Simple and Efficient Federated Recommender System

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