Evaluating Multi-Global Server Architecture for Federated Learning
Asfia Kawnine Hung Cao Atah Nuh Mih Monica Wachowicz
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.