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

Bibliogr.: 30 ref

Open Access

scientific publication

teaduspublikatsioon

classifier

1.1

Scopus

Journal metrics at Scopus

Article at Scopus

WOS

Journal metrics at WOS

Article at WOS

category (general)

Physics and astronomy

Engineering

Chemistry

Computer science

Biochemistry, genetics and molecular biology

category (sub)

Physics and astronomy. Instrumentation

Engineering. Electrical and electronic engineering

Physics and astronomy. Atomic and molecular physics, and optics

Chemistry. Analytical chemistry

Computer science. Information systems

Biochemistry, genetics and molecular biology. Biochemistry

kvartiil

TTÜ department

Thomas Johann Seebecki elektroonikainstituut

language

inglise

Uurimisrühm

Communication systems research group

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