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Conference Paper
Federated Learning for User Mobility Classification in 5G Heterogeneous Networks
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- Authors
- Syed Maaz Shahid, SungKyung Kim, Sungoh Kwon
- Issue Date
- 2024-06
- Citation
- Vehicular Technology Conference (VTC) 2024 (Spring), pp.1-6
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this work, we propose a distributed learning framework that classifies users' transportation modes by leveraging heterogeneous networks (HetNets) architecture and employing a federated learning (FL) algorithm. The ultra-densification of small cells and the dynamic mobility of users impact performance by triggering unnecessary handovers. Therefore, information on user mobility allows the network to perform handover management more intelligently and efficiently. The proposed machine learning framework adopts distributed learning using a federated learning algorithm to detect transportation modes, including driving a car, riding a bicycle, walking, and running. In the proposed framework, local models are trained at small cells using user history information inherently distributed on the network side. A macro cell aggregates the local models to get a global model for classifying the transportation modes of users. Training the local models by small cells over user history information addresses critical FL issues, such as non-independent and identically distributed data and system heterogeneity. Simulation results demonstrate that the proposed framework achieves an accuracy of 98.85% in classifying transportation modes, utilizing input features extracted from user history information.
- KSP Keywords
- Cell aggregates, Dynamic Mobility, Federated learning, Global model, Handover management, History information, Input features, Learning framework, Local models, Macro cell, Mobility classification