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.

本文提出了一种新的框架，叫做联合边缘智能（FEI），以评估物联网网络的能量成本和边缘服务器的本地数据处理能力，从而使边缘服务器适时地请求足够训练令人满意的模型所需的数据。同时，本文引入了映射函数来评估边缘服务器的计算负载，最后采用基于ADMM的方法来优化物联网网络的能量成本和边缘服务器的平均计算资源利用率。该文证明，所提出的算法不会泄露任何数据，也不会泄漏物联网网络的拓扑信息。仿真结果表明，FEI框架可以在有限的牺牲模型收敛性能的情况下，显著提高物联网网络和边缘服务器的资源效率。

物联网网络中联合边缘智能的资源效率优化