论文标题
优化物联网网络中联合边缘智能的资源效率
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks
论文作者
论文摘要
本文研究了一个基于边缘智能的物联网网络，其中一组Edge服务器基于从多技术支持的IoT网络上载的数据集中使用联合学习（FL）学习共享模型。物联网网络的数据上传性能和边缘服务器的计算能力在影响FL模型训练过程的情况下互相纠缠。我们提出了一个名为Federated Edge Intelligence（FEI）的新型框架，该框架允许Edge服务器根据IoT网络的能源成本及其本地数据处理能力评估所需的数据样本数量，并且仅要求提供足以培训令人满意模型的数据量。当两个广泛使用的物联网解决方案（例如5G nb-iot）和无牌带IoT（例如Wi-Fi，Zigbee和5G NR-U）提供两个广泛使用的物联网解决方案时，我们评估了数据上传的能源成本。我们证明，整个物联网网络的成本最小化问题是可分开的，可以分为一组子问题，每个子问题都可以通过单个边缘服务器解决。我们还引入了一个映射功能，以量化边缘服务器的计算负载，在三个关键参数的不同组合下：数据集的大小，本地批处理大小和本地培训通行证的数量。最后，我们采用了乘数的替代方向方法（ADMM）的方法共同优化了IoT网络的能源成本和Edge服务器的平均计算资源利用。我们证明我们提出的算法不会引起任何数据泄漏，也不会披露IoT网络的任何拓扑信息。仿真结果表明，我们提出的框架大大提高了物联网网络和边缘服务器的资源效率，而模型收敛性能的牺牲只有有限的牺牲。
This paper studies an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) based on the datasets uploaded from a multi-technology-supported IoT network. The data uploading performance of IoT network and the computational capacity of edge servers are entangled with each other in influencing the FL model training process. We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network as well as their local data processing capacity and only request the amount of data that is sufficient for training a satisfactory model. We evaluate the energy cost for data uploading when two widely-used IoT solutions: licensed band IoT (e.g., 5G NB-IoT) and unlicensed band IoT (e.g., Wi-Fi, ZigBee, and 5G NR-U) are available to each IoT device. We prove that the cost minimization problem of the entire IoT network is separable and can be divided into a set of subproblems, each of which can be solved by an individual edge server. We also introduce a mapping function to quantify the computational load of edge servers under different combinations of three key parameters: size of the dataset, local batch size, and number of local training passes. Finally, we adopt an Alternative Direction Method of Multipliers (ADMM)-based approach to jointly optimize energy cost of the IoT network and average computing resource utilization of edge servers. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network. Simulation results show that our proposed framework significantly improves the resource efficiency of the IoT network and edge servers with only a limited sacrifice on the model convergence performance.
