Manzoor, H. U., Khan, A. R., Flynn, D., Alam, M. M., Akram, M., Imran, M. A., Zoha, A.

FedBranched : leveraging federated learning for anomaly-aware load forecasting in energy networks

author

Manzoor, Habib Ullah

Khan, Ahsan Raza

Flynn, David

Alam, Muhammad Mahtab

Akram, Muhammad

Imran, Muhammad Ali

Zoha, Ahmed

statement of authorship

Habib Ullah Manzoor, Ahsan Raza Khan, David Flynn, Muhammad Mahtab Alam, Muhammad Akram, Muhammad Ali Imran and Ahmed Zoha

source

Sensors

publisher

MDPI

journal volume number month

vol. 23, 7

year of publication

2023

pages

art. 3570

url

https://doi.org/10.3390/s23073570

subject term

andmed

tehisõpe

klastrid

tehisnärvivõrgud

keyword

federated learning (FL)

artificial neural networks (ANN)

clustering

machine learning (ML)

ISSN

1424-8220

notes

Bibliogr.: 30 ref

Special Issue: Machine Learning Techniques for Energy Efficient IoT Networks

Open Access

Open Access (kuldne)

scientific publication

teaduspublikatsioon

classifier

1.1

Scopus

TTÜ department

Thomas Johann Seebecki elektroonikainstituut

language

inglise

Manzoor, H. U., Khan, A. R., Flynn, D., Alam, M. M., Akram, M., Imran, M. A., Zoha, A. FedBranched : leveraging federated learning for anomaly-aware load forecasting in energy networks // Sensors (2023) vol. 23, 7, art. 3570. https://doi.org/10.3390/s23073570