Eecs-fl: energy-efficient client selection for federated learning in AIoT
Abstract The Artificial Intelligence of Things (AIoT) ecosystem faces significant challenges related to limited client energy budgets and resource heterogeneity, particularly when employing the Federated Learning (FL) framework. This paper presents a novel energy-efficient client selection algorithm...
Saved in:
| Main Authors: | Yiyang Zhang, Yiming Luo, Tao Yang, Xiaofeng Wu, Bo Hu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-03-01
|
| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-025-02435-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrating AIoT Technologies in Aquaculture: A Systematic Review
by: Fahmida Wazed Tina, et al.
Published: (2025-04-01) -
Research on the Evaluation and Selection of AIoT Suppliers from an ESG Perspective
by: Xiaoyue You, et al.
Published: (2025-06-01) -
Energy Efficiency of Kernel and User Space Level VPN Solutions in AIoT Networks
by: ALEKSANDAR JEVREMOVIC, et al.
Published: (2025-01-01) -
AIoT-Enabled Data Management for Smart Agriculture: A Comprehensive Review on Emerging Technologies
by: Xu Luo, et al.
Published: (2025-01-01) -
Platform framework for blockchain-enhanced healthcare AIoT systems
by: Minhee Jun
Published: (2025-04-01)