The paper introduces an energy-efficient federated edge learning framework that quantifies learning loss from sample counts, applies stochastic online adaptation, and solves resource optimization with convergence bounds to improve performance in IoT networks.
Combini ng federated learning and edge computing toward ubiquitous in telligence in 6G network: Challenges, recent advances, and future direct ions
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Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks
The paper introduces an energy-efficient federated edge learning framework that quantifies learning loss from sample counts, applies stochastic online adaptation, and solves resource optimization with convergence bounds to improve performance in IoT networks.