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.

中文翻译：

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为物联网网络中的联合边缘智能优化资源效率

本文研究了基于边缘情报的IoT网络，其中一组边缘服务器基于从多技术支持的IoT网络上传的数据集，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模型训练过程中相互纠缠。我们提出了一个称为联邦边缘智能（FEI）的新颖框架，该框架允许边缘服务器根据IoT网络的能源成本以及它们的本地数据处理能力来评估所需的数据样本数，并且仅请求数据量这足以训练出满意的模型。当使用两种广泛使用的IoT解决方案：许可频段IoT（例如，每个IoT设备都可以使用5G NB-IoT）和非许可频段的IoT（例如Wi-Fi，ZigBee和5G NR-U）。我们证明了整个IoT网络的成本最小化问题是可分离的，可以分为一组子问题，每个子问题都可以由单独的边缘服务器解决。我们还引入了一种映射函数，以量化三个关键参数（数据集的大小，本地批处理大小和本地训练次数）的不同组合下边缘服务器的计算负荷。最后，我们采用基于乘数的替代方向方法（ADMM）的方法，共同优化IoT网络的能源成本和边缘服务器的平均计算资源利用率。我们证明了我们提出的算法不会引起任何数据泄漏，也不会泄露IoT网络的任何拓扑信息。