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

Abstract

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.