AI RESEARCH
Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks
arXiv CS.LG
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ArXi:2604.10662v1 Announce Type: new Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data, especially in small-scale datasets, is challenging, and independent edge nodes can lead to inefficient resource utilization and reduced learning performance. To address these issues, this paper proposes a collaborative optimization framework for energy-efficient federated edge learning with small-scale datasets.