Federated Learning in Mobile and Edge Environments for Telecom Use Cases
Author(s): Varinder Kumar Sharma
Publication #: 2508026
Date of Publication: 05.01.2024
Country: United States
Pages: 1-10
Published In: Volume 10 Issue 1 January-2024
DOI: https://doi.org/10.5281/zenodo.17062956
Abstract
The proliferation of mobile devices, IoT endpoints, and edge computing nodes in modern telecommunication infrastructures, particularly within the context of 5G and emerging 6G networks, has resulted in the generation of vast, distributed, and highly sensitive datasets. These data sources are vital for enabling intelligent services such as anomaly detection in mobile networks, predictive maintenance of telecom equipment, real-time traffic forecasting, and personalized customer experience optimization. However, traditional centralized machine learning (ML) paradigms are ill-suited for handling such data due to stringent latency requirements, limited bandwidth availability, privacy regulations (e.g., the General Data Protection Regulation, or GDPR), and computational heterogeneity across devices. In response to these limitations, Federated Learning (FL) has emerged as a promising distributed ML approach that allows multiple edge and mobile devices to collaboratively train global models without sharing raw data. This decentralized learning paradigm ensures that data remains on-device, thereby enhancing privacy while minimizing bandwidth usage and reducing inference latency.
This paper presents a comprehensive design and performance analysis of Federated Learning in mobile and edge environments with a specific focus on telecom use cases. We propose a robust hierarchical FL architecture that integrates three layers of computation: the client layer (smartphones, sensors, IoT devices), the edge layer (Mobile Edge Computing nodes co-located with 5G base stations), and a centralized cloud coordination layer. Our proposed framework incorporates advanced strategies for client selection based on computational resources, connection stability, and trust metrics. A hybrid reputation-based mechanism is utilized to exclude malicious or unreliable clients, thereby maintaining the integrity of global model updates. Moreover, the system leverages edge-level personalization techniques to fine-tune global models to fit local environments, thereby significantly improving performance under non-independent and identically distributed (non-IID) data conditions, a common characteristic in telecom systems.
Keywords: Federated Learning, Mobile Edge Computing, 5G Networks, 6G Readiness, Telecom Use Cases, Hierarchical FL, Non-IID Data, Privacy-Preserving AI, Edge Intelligence, Network Anomaly Detection, Client Selection, Model Personalization, Resource-Aware Scheduling, Distributed Machine Learning, Trust-Based Participation
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