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

Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT  Networks

The Internet of things (IoT) is still in its infancy and has attracted much
interest in many industrial sectors including medical fields, logistics
tracking, smart cities and automobiles. However as a paradigm, it is
susceptible to a range of significant intrusion threats. This paper presents a
threat analysis of the IoT and uses an Artificial Neural Network (ANN) to
combat these threats. A multi-level perceptron, a type of supervised ANN, is
trained using internet packet traces, then is assessed on its ability to thwart
Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the
classification of normal and threat patterns on an IoT Network. The ANN
procedure is validated against a simulated IoT network. The experimental
results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS
attacks.

本文针对物联网（IoT）的威胁进行了分析，使用人工神经网络（ANN）来对抗攻击，着重于对 IoT 网络上正常和威胁模式的分类，并使用模拟的 IoT 网络来验证其有效性。实验结果表明，该 ANN 程序能够成功检测到各种 DDoS/DoS 攻击，准确率达 99.4%。