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.
DASP: hierarchical offline reinforcement learning via diffusion autodecoder and skil l primitive,
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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.