Securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks

M. Manimegalai; K. Sebasthirani; P. Maruthupandi; G. Rajesh

doi:10.1007/978-981-97-8836-1_7

ScienceGate Book Chapters

BOOK-CHAPTER

Securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks

M. Manimegalai K. Sebasthirani P. Maruthupandi G. Rajesh

Year: 2025 Lecture notes in networks and systems Pages: 77-95 Publisher: Springer International Publishing

DOI: 10.1007/978-981-97-8836-1_7

Get Full-Text PDF Get Analytical Report

Keywords:

Anomaly detection Computer science Federated learning Anomaly (physics) Smart grid Artificial intelligence Distributed computing Engineering Electrical engineering

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.15

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Smart Grid Security and Resilience

Physical Sciences → Engineering → Control and Systems Engineering

Electricity Theft Detection Techniques

Physical Sciences → Engineering → Electrical and Electronic Engineering

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks

Metrics

Topics

Related Documents

Privacy-Preserving Anomaly Detection Using Vertical Federated Learning in Smart Grids

Decentralized water quality classification using federated learning with recurrent neural networks

Multi-Source Information Fusion for Anomaly Detection in Smart Grids Using Federated Learning

FedADSN: Anomaly detection for social networks under decentralized federated learning

Decentralized Federated Anomaly Detection in Mobile Networks