Privacy-preserving and Efficient Decentralized Federated Learning-based Energy Theft Detector-Reference-Cited by-同舟云学术

Privacy-preserving and Efficient Decentralized Federated Learning-based Energy Theft Detector

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:GLOBECOM 2022 - 2022 IEEE Global Communications Conference
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Author:

Ibrahem Mohamed I.¹,Mahmoud Mohamed²,Fouda Mostafa M.³,ElHalawany Basem M.⁴,Alasmary Waleed⁵

Affiliation:

1. George Mason University,Department of Cyber Security Engineering,Fairfax,VA,USA

2. Tennessee Tech University,Department of Electrical and Computer Engineering,Cookeville,TN,USA

3. Idaho State University,Department of Electrical and Computer Engineering,Pocatello,ID,USA,83209

4. Benha University,Faculty of Engineering at Shoubra,Department of Electrical Engineering,Cairo,Egypt,11672

5. Umm Al-Qura University,Department of Computer Engineering,Makkah,Saudi Arabia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10000063/10000593/10000881.pdf?arnumber=10000881

Reference15 articles.

1. Multi-input functional encryption for inner products: Function-hiding realizations and constructions without pairings;abdalla;Advances in Cryptology,2018

2. Federated Learning in Mobile Edge Networks: A Comprehensive Survey

3. Simple Functional Encryption Schemes for Inner Products

4. Privacy-Preserving Federated-Learning-Based Net-Energy Forecasting

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