@article{ART003228266},
author={Ikjune Yoon and Dong Kun Noh},
title={Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2025},
volume={30},
number={7},
pages={11-19}
TY - JOUR
AU - Ikjune Yoon
AU - Dong Kun Noh
TI - Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems
JO - Journal of The Korea Society of Computer and Information
PY - 2025
VL - 30
IS - 7
PB - The Korean Society Of Computer And Information
SP - 11
EP - 19
SN - 1598-849X
AB - With recent advancements in energy harvesting technologies and the proliferation of AIoT devices capable of on-device learning, energy-harvesting AIoT systems have attracted significant attention. This study proposes an energy-adaptive approximate computing method designed to efficiently enhance federated learning performance while ensuring the stable operation of energy-constrained edge AIoT devices. Typically, there is a proportional relationship between learning performance and energy consumption; thus, performance trade-offs are inevitable under stringent energy conditions. Based on a solar energy harvesting model, the proposed scheme optimizes local training data sizes and parameter exchange volumes for each training round within limited energy budgets, performing approximate computing. This approach effectively minimizes device blackout durations, thereby improving the overall accuracy of federated learning.
KW - AIoT;energy-harvesting;approximate computing;federated learning;energy-adaptive;availability
DO -
UR -
ER -
Ikjune Yoon and Dong Kun Noh. (2025). Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems. Journal of The Korea Society of Computer and Information, 30(7), 11-19.
Ikjune Yoon and Dong Kun Noh. 2025, "Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems", Journal of The Korea Society of Computer and Information, vol.30, no.7 pp.11-19.
Ikjune Yoon, Dong Kun Noh "Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems" Journal of The Korea Society of Computer and Information 30.7 pp.11-19 (2025) : 11.
Ikjune Yoon, Dong Kun Noh. Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems. 2025; 30(7), 11-19.
Ikjune Yoon and Dong Kun Noh. "Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems" Journal of The Korea Society of Computer and Information 30, no.7 (2025) : 11-19.
Ikjune Yoon; Dong Kun Noh. Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems. Journal of The Korea Society of Computer and Information, 30(7), 11-19.
Ikjune Yoon; Dong Kun Noh. Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems. Journal of The Korea Society of Computer and Information. 2025; 30(7) 11-19.
Ikjune Yoon, Dong Kun Noh. Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems. 2025; 30(7), 11-19.
Ikjune Yoon and Dong Kun Noh. "Energy-Adaptive Approximate Computing Scheme for Efficient Federated Learning in Energy-Harvesting AIoT Systems" Journal of The Korea Society of Computer and Information 30, no.7 (2025) : 11-19.
