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.
Coded computing for low-latency federated learning over wireless edge networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.