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
Scopus
TTÜ department
Thomas Johann Seebecki elektroonikainstituut
language
inglise
Uurimisrühm
Communication systems research group