  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.
