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Decentralized Federated Learning with Node Incentive and Role Switching Mechanism for Network Traffic Prediction in NFV Environment

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Publicat a:Symmetry vol. 17, no. 6 (2025), p. 970-996
Autor principal: Hu, Ying
Altres autors: Liu, Ben, Li, Jianyong, Jia Linlin
Publicat:
MDPI AG
Matèries:
Network function virtualization
Software
Accuracy
Deep learning
Network management systems
Forecasting
Communication
Prediction models
Optimization techniques
Communications traffic
Manuscripts
Neural networks
Nodes
Traffic flow
Blockchain
Systems stability
Privacy
Time series
Federated learning
Fuzzy logic
Resource management
Accés en línia:Citation/Abstract
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Resum:In network function virtualization (NFV) environments, dynamic network traffic prediction with unique symmetric and asymmetric traffic patterns is critical for efficient resource orchestration and service chain optimization. Traditional centralized prediction models face risks of cross-provider data privacy leakage when network service providers collaborate with resource providers to deliver services. To address this issue, we propose a decentralized federated learning method for network traffic prediction, which ensures that historical network traffic data remain stored locally without requiring cross-provider sharing. To further mitigate interference from malicious provider behaviors on network traffic prediction, we design a node incentive mechanism that dynamically adjusts node roles (e.g., “Aggregator”, “Worker Node”, “Residual Node”, and “Evaluator”). When a node exhibits malicious behavior, its contribution score is reduced; otherwise, it is rewarded. Simulation experiments conducted on an NFV platform using public network traffic datasets demonstrate that the proposed method maintains prediction accuracy even in scenarios with a high proportion of malicious nodes, alleviates their adverse effects, and ensures prediction stability.
ISSN:2073-8994
DOI:10.3390/sym17060970
Font:Engineering Database