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.

基于元胞自动机的客户端选择算法（CA-CS）是一种针对联邦学习的客户端选择算法，能够在数据流中智能选择客户端，并在实时场景中取得可比较的准确率，同时避免高延迟客户端。

基于元胞自动机的联邦学习智能客户端选择

Intelligent Client Selection for Federated Learning using Cellular  Automata

We consider the latency minimization problem in a task-offloading scenario,
where multiple servers are available to the user equipment for outsourcing
computational tasks. To account for the temporally dynamic nature of the
wireless links and the availability of the computing resources, we model the
server selection as a multi-armed bandit (MAB) problem. In the considered MAB
framework, rewards are characterized in terms of the end-to-end latency. We
propose a novel online learning algorithm based on the principle of optimism in
the face of uncertainty, which outperforms the state-of-the-art algorithms by
up to ~1s. Our results highlight the significance of heavily discounting the
past rewards in dynamic environments.

本文提出一种基于乐观性策略的在线学习算法，用于解决任务卸载场景下的多臂老虎机问题，以实现最小化端到端延迟。研究结果表明，在动态环境中，严重贬低过去的奖励的重要性。该算法表现优于现有算法，优化效果高达约 1 秒。

非平稳环境中的计算卸载在线算法

An Online Algorithm for Computation Offloading in Non-Stationary  Environments

By offloading intensive computation tasks to the edge cloud located at the
cellular base stations, mobile-edge computation offloading (MECO) has been
regarded as a promising means to accomplish the ambitious millisecond-scale
end-to-end latency requirement of the fifth-generation networks. In this paper,
we investigate the latency-minimization problem in a multi-user time-division
multiple access MECO system with joint communication and computation resource
allocation. Three different computation models are studied, i.e., local
compression, edge cloud compression, and partial compression offloading. First,
closed-form expressions of optimal resource allocation and minimum system delay
for both local and edge cloud compression models are derived. Then, for the
partial compression offloading model, we formulate a piecewise optimization
problem and prove that the optimal data segmentation strategy has a piecewise
structure. Based on this result, an optimal joint communication and computation
resource allocation algorithm is developed. To gain more insights, we also
analyze a specific scenario where communication resource is adequate while
computation resource is limited. In this special case, the closed-form solution
of the piecewise optimization problem can be derived. Our proposed algorithms
are finally verified by numerical results, which show that the novel partial
compression offloading model can significantly reduce the end-to-end latency.

本文旨在研究移动边缘计算卸载系统（MECO）中资源分配与压缩模型对系统延迟的影响，并通过数值模拟验证了不同模型的有效性。