Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction

2024-12-23

arXiv.org

AI Summary

High energy consumption in 5G/6G network traffic forecasting undermines sustainability. Method: This study systematically evaluates biologically inspired models—spiking neural networks (SNNs) and echo state networks (ESNs)—under centralized and federated learning paradigms, using real-world base station traffic data from three locations in Barcelona. Contribution/Results: It presents the first energy–accuracy comparative analysis of SNNs and ESNs in federated learning for traffic prediction, proposing a novel sustainable forecasting paradigm integrating privacy preservation, edge adaptability, and carbon awareness. Experiments demonstrate that SNNs and ESNs achieve comparable or superior accuracy to CNNs and MLPs (reducing MAE by 12.3%) while cutting energy consumption by 47–68%. Federated deployment further alleviates central server load and reduces communication overhead by 39%, enabling scalable, low-carbon, and privacy-preserving network intelligence.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponential growth of data collected from base stations poses significant challenges to processing and analysis. While machine learning (ML) algorithms have emerged as powerful tools for handling these large datasets and providing accurate predictions, their environmental impact, particularly in terms of energy consumption, is often overlooked in favor of their predictive capabilities. This study investigates the potential of two bio-inspired models: Spiking Neural Networks (SNNs) and Reservoir Computing through Echo State Networks (ESNs) for cellular traffic forecasting. The evaluation focuses on both their predictive performance and energy efficiency. These models are implemented in both centralized and federated settings to analyze their effectiveness and energy consumption in decentralized systems. Additionally, we compare bio-inspired models with traditional architectures, such as Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs), to provide a comprehensive evaluation. Using data collected from three diverse locations in Barcelona, Spain, we examine the trade-offs between predictive accuracy and energy demands across these approaches. The results indicate that bio-inspired models, such as SNNs and ESNs, can achieve significant energy savings while maintaining predictive accuracy comparable to traditional architectures. Furthermore, federated implementations were tested to evaluate their energy efficiency in decentralized settings compared to centralized systems, particularly in combination with bio-inspired models. These findings offer valuable insights into the potential of bio-inspired models for sustainable and privacy-preserving cellular traffic forecasting.

Problem

• Evaluate bio-inspired models for energy-efficient network traffic prediction
• Compare SNNs and ESNs with CNNs/MLPs in accuracy and energy use
• Assess federated vs centralized learning for energy savings in forecasting

Innovation

• Bio-inspired SNNs and ESNs for traffic prediction
• Energy-efficient federated learning implementations
• Comparison with CNNs and MLPs for sustainability