Poisoning Attack Mitigation for Privacy-Preserving Federated Learning-Based Energy Theft Detection-Reference-Cited by-同舟云学术

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Poisoning Attack Mitigation for Privacy-Preserving Federated Learning-Based Energy Theft Detection

Published:2024-06-09 Issue: Volume: Page:3962-3968
ISSN:
Container-title:ICC 2024 - IEEE International Conference on Communications
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Author:

Srewa Mahmoud¹,Winfree Michaela F.²,Ibrahem Mohamed I.³,Nabil Mahmoud⁴,Lu Rongxing⁵,Alsharif Ahmad¹

Affiliation:

1. University of Alabama,Department of Computer Science,AL,USA,35487

2. University of Kentucky,Department of Computer Science,KY,USA,40506

3. School of Computer and Cyber Sciences, Augusta University,Augusta,GA,USA,30912

4. University of North Carolina A&T,Electrical and Computer Engineering,NC,USA,27411

5. University of New Brunswick,Faculty of Computer Science,Fredericton,NB,Canada,E3B 5A3

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10622104/10622158/10622191.pdf?arnumber=10622191

Reference15 articles.

1. Performance Analysis of Electricity Theft Detection for the Smart Grid: An Overview

2. Review of various modeling techniques for the detection of electricity theft in smart grid environment

3. Electricity-Theft Detection for Change-and-Transmit Advanced Metering Infrastructure

4. EPIC: Efficient Privacy-Preserving Scheme With EtoE Data Integrity and Authenticity for AMI Networks

5. Efficient Privacy-Preserving Electricity Theft Detection With Dynamic Billing and Load Monitoring for AMI Networks

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