Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communications, and healthcare. FL endeavors to bring Machine Learning (ML) down to the edge by harnessing data from million of devices and IoT sensors, thus enabling rapid responses to dynamic environments and yielding highly personalized results. However, the increased amount of sensors across diverse applications poses challenges in terms of communication and resource allocation, hindering the participation of all devices in the federated process and prompting the need for effective FL client selection. To address this issue, we propose Cellular Automaton-based Client Selection (CA-CS), a novel client selection algorithm, which leverages Cellular Automata (CA) as models to effectively capture spatio-temporal changes in a fast-evolving environment. CA-CS considers the computational resources and communication capacity of each participating client, while also accounting for inter-client interactions between neighbors during the client selection process, enabling intelligent client selection for online FL processes on data streams that closely resemble real-world scenarios. In this paper, we present a thorough evaluation of the proposed CA-CS algorithm using MNIST and CIFAR-10 datasets, while making a direct comparison against a uniformly random client selection scheme. Our results demonstrate that CA-CS achieves comparable accuracy to the random selection approach, while effectively avoiding high-latency clients.

翻译：联邦学习（FL）已成为交通、通信和医疗等众多实际应用中实现隐私增强和延迟最小化的有效解决方案。FL通过利用来自数百万设备和物联网传感器的数据，将机器学习（ML）推向边缘计算，从而实现对动态环境的快速响应并生成高度个性化的结果。然而，跨多种应用场景的传感器数量激增带来了通信和资源分配方面的挑战，阻碍了所有设备参与联邦过程，亟需有效的FL客户端选择机制。针对这一问题，我们提出基于元胞自动机的客户端选择算法（CA-CS），该算法创新性地利用元胞自动机（CA）作为模型，有效捕获快速演化环境中的时空变化。CA-CS在客户端选择过程中综合考虑各参与客户端的计算资源和通信能力，同时兼顾邻居客户端间的交互作用，从而实现对接近真实场景数据流的在线FL流程进行智能客户端选择。本文使用MNIST和CIFAR-10数据集对提出的CA-CS算法进行充分评估，并与均匀随机客户端选择方案进行直接对比。实验结果表明，CA-CS在达到与随机选择方法相当准确率的同时，有效规避了高延迟客户端。