Distributed Core Network Traffic Prediction Architecture Based on Vertical Federated Learning

• Published in: Proceedings of the 13th International Conference on Computer Engineering and Networks Vol. 1127; pp. 230 - 237
• Main Authors: Li, Pengyu, Guo, Chengwei, Xing, Yanxia, Shi, Yingji, Feng, Lei, Zhou, Fanqin
• Format: Book Chapter
• Language: English
• Published: Singapore Springer Nature Singapore 01.01.2024; Springer Singapore Pte. Limited
• Series: Lecture Notes in Electrical Engineering
• Subjects: Core network; Federated learning; Split learning; Traffic prediction
• ISBN: 981999246X; 9789819992461
• ISSN: 1876-1100; 1876-1119
• DOI: 10.1007/978-981-99-9247-8_23

Network traffic prediction has always been an important research topic, frequently employed in intelligent network operations for load awareness, re-source management, and predictive control. Most existing methods adopt a centralized training and deployment approach, neglecting the involvement of multiple parties in the prediction process and the potential for training prediction models using distributed methods. This study introduces a novel wireless traffic prediction framework based on split learning, addressing the limitations of existing centralized methods. The proposed framework enables multiple edge clients to collaboratively train high-quality prediction models without transmitting large amounts of data, thus mitigating latency and privacy concerns. Each participant trains a dimension-specific prediction model using its local data, which are then aggregated through a collaborative interaction process. A partially global model is trained and shared among clients to tackle statistical heterogeneity challenges. Experimental results on real-world wireless traffic datasets demonstrate that our approach outperforms state-of-the-art methods, showing its potential and accuracy in Internet traffic prediction.