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...

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Hlavní autoři:	Greeshma Sarath, M. Geetha, Jyothisha J. Nair
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2026-01-01
Edice:	IEEE Open Journal of the Communications Society
Témata:	Adaptive local training communication efficiency discrete cosine transform federated learning federated aggregation strategy non-IID data distribution
On-line přístup:	https://ieeexplore.ieee.org/document/11534151/
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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

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