Nguyen, K.A.; Nguyen, Q.A.; Hong, J.-P. Federated Learning in Small-Cell Networks: Stochastic Geometry-Based Analysis on the Required Base Station Density. Sensors2023, 23, 7184.
Nguyen, K.A.; Nguyen, Q.A.; Hong, J.-P. Federated Learning in Small-Cell Networks: Stochastic Geometry-Based Analysis on the Required Base Station Density. Sensors 2023, 23, 7184.
Nguyen, K.A.; Nguyen, Q.A.; Hong, J.-P. Federated Learning in Small-Cell Networks: Stochastic Geometry-Based Analysis on the Required Base Station Density. Sensors2023, 23, 7184.
Nguyen, K.A.; Nguyen, Q.A.; Hong, J.-P. Federated Learning in Small-Cell Networks: Stochastic Geometry-Based Analysis on the Required Base Station Density. Sensors 2023, 23, 7184.
Abstract
Recently, federated learning (FL) has been receiving great attention as an effective machine learning method to avoid the security issue in raw data collection as well as to distribute the computing load to edge devices. However, even though wireless communications is a essential part for implementing FL in edge networks, there have been a few works that analyze the effect of wireless networks on FL. In this paper, we investigate FL in small-cell networks where multiple base stations (BSs) and users are located according to homogeneous Poisson point process (PPP) with different densities. We comprehensively analyze the effects of geographic node deployment on the model aggregation in FL on the basis of stochastic geometry-based analysis. We derive the closed-form expressions of coverage probability with tractable approximations and discuss the minimum required BS density for achieving a target model aggregation rate in small-cell networks. Our analysis and simulation results provide insightful information for understanding the behaviors of FL in small-cell networks, and it can be exploited as a guideline for designing the network facilitating the wireless FL.
Keywords
federated learning; small-cell networks; stochastic geometry; base station density; Poisson point process
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
