Federated learning (FL) with a single global server framework is currently a
popular approach for training machine learning models on decentralized
environment, such as mobile devices and edge devices. However, the centralized
server architecture poses a risk as any challenge on the central/global server
would result in the failure of the entire system. To minimize this risk, we
propose a novel federated learning framework that leverages the deployment of
multiple global servers. We posit that implementing multiple global servers in
federated learning can enhance efficiency by capitalizing on local
collaborations and aggregating knowledge, and the error tolerance in regard to
communication failure in the single server framework would be handled. We
therefore propose a novel framework that leverages the deployment of multiple
global servers. We conducted a series of experiments using a dataset containing
the event history of electric vehicle (EV) charging at numerous stations. We
deployed a federated learning setup with multiple global servers and client
servers, where each client-server strategically represented a different region
and a global server was responsible for aggregating local updates from those
devices. Our preliminary results of the global models demonstrate that the
difference in performance attributed to multiple servers is less than 1%. While
the hypothesis of enhanced model efficiency was not as expected, the rule for
handling communication challenges added to the algorithm could resolve the
error tolerance issue. Future research can focus on identifying specific uses
for the deployment of multiple global servers.

提出了一种利用多个全局服务器部署的新型联邦学习框架，旨在增强模型效率、处理通信错误容忍问题，并为未来研究提供了方向。

评估联邦学习的多全球服务器架构

Evaluating Multi-Global Server Architecture for Federated Learning

As Machine Learning (ML) is increasingly used in solving various tasks in
real-world applications, it is crucial to ensure that ML algorithms are robust
to any potential worst-case noises, adversarial attacks, and highly unusual
situations when they are designed. Studying ML robustness will significantly
help in the design of ML algorithms. In this paper, we investigate ML
robustness using adversarial training in centralized and decentralized
environments, where ML training and testing are conducted in one or multiple
computers. In the centralized environment, we achieve a test accuracy of 65.41%
and 83.0% when classifying adversarial examples generated by Fast Gradient Sign
Method and DeepFool, respectively. Comparing to existing studies, these results
demonstrate an improvement of 18.41% for FGSM and 47% for DeepFool. In the
decentralized environment, we study Federated learning (FL) robustness by using
adversarial training with independent and identically distributed (IID) and
non-IID data, respectively, where CIFAR-10 is used in this research. In the IID
data case, our experimental results demonstrate that we can achieve such a
robust accuracy that it is comparable to the one obtained in the centralized
environment. Moreover, in the non-IID data case, the natural accuracy drops
from 66.23% to 57.82%, and the robust accuracy decreases by 25% and 23.4% in
C&W and Projected Gradient Descent (PGD) attacks, compared to the IID data
case, respectively. We further propose an IID data-sharing approach, which
allows for increasing the natural accuracy to 85.04% and the robust accuracy
from 57% to 72% in C&W attacks and from 59% to 67% in PGD attacks.

研究了机器学习的鲁棒性，使用集中式和分散式环境进行对抗训练，结果显示比现有研究提高了 18.41% 和 47% 的准确性，并提出了独立同分布和非独立同分布数据的 IID 数据共享方法，可提高自然准确性和鲁棒准确性。