Toward Lightweight, Communication-Efficient, and Energy-Aware Federated Learning Under Non-IID Settings

Federated learning (FL) has emerged as a promising privacy-preserving methodology for cooperatively training machine learning models across numerous machines without transferring sensitive data between them. However, the substantial communication overhead and diverse client contributions hinder its...

Whakaahuatanga katoa

I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Greeshma Sarath, M. Geetha, Jyothisha J. Nair
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	IEEE 2026-01-01
Rangatū:	IEEE Open Journal of the Communications Society
Ngā marau:	Adaptive local training communication efficiency discrete cosine transform federated learning federated aggregation strategy non-IID data distribution
Urunga tuihono:	https://ieeexplore.ieee.org/document/11534151/
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Toward Lightweight, Communication-Efficient, and Energy-Aware Federated Learning Under Non-IID Settings

Ngā tūemi rite