针对小数据集的物联网边缘学习，提出节能协同优化框架。
Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks
- 基于样本数与损失关系建模，动态调整学习策略。
- 实测在碰撞避让任务中提升学习性能与资源效率。
- 适合资源受限的智能城市、自动驾驶等场景。
大规模物联网网络支持智慧城市、自动驾驶等智能服务，但常面临资源约束问题。收集异构传感数据，尤其是小规模数据集时尤为困难，独立的边缘节点易导致资源利用低效和学习性能下降。为此，本文提出一种面向小规模数据集的节能联邦边缘学习协同优化框架。首先推导出期望学习损失，量化训练样本数量与学习目标之间的关系；设计一种随机在线学习算法以适应数据变化；构建具有收敛性保障的资源优化问题；最终提出一种高效的分布式在线算法，可规模化求解大规模优化问题。大量仿真及自主导航碰撞避让案例表明，所提方法相较现有最优基准显著提升了学习性能与资源效率。
原文摘要 · Abstract (English)
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. We first derive an expected learning loss to quantify the relationship between the number of training samples and learning objectives. A stochastic online learning algorithm is then designed to adapt to data variations, and a resource optimization problem with a convergence bound is formulated. Finally, an online distributed algorithm efficiently solves large-scale optimization problems with high scalability. Extensive simulations and autonomous navigation case studies with collision avoidance demonstrate that the proposed approach significantly improves learning performance and resource efficiency compared to state-of-the-art benchmarks.
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