Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction

2025-01-01

IEEE Transactions on Cognitive Communications and Networking

AI Summary

To address the high communication overhead and difficulty in modeling spatial dependencies in edge-side wireless traffic forecasting under federated learning (FL), this paper proposes a joint optimization framework integrating gradient compression and correlation-driven aggregation. It is the first to introduce gradient sparsification into wireless traffic prediction; designs three correlation-aware aggregation strategies to explicitly model cross-device spatial dependencies; and incorporates error feedback with gradient tracking to ensure convergence under compression. Evaluated on two real-world datasets, the method achieves prediction accuracy comparable to state-of-the-art (SOTA) approaches while reducing communication volume by up to 100×, significantly enhancing FL deployment efficiency in resource-constrained wireless networks. Key innovations include: (i) synergistic co-design of gradient sparsification and spatial correlation modeling, and (ii) a lightweight FL optimization paradigm tailored for spatiotemporal traffic forecasting.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. Code is available at https://github.com/chuanting/FedGCC.

Problem

• Federated Learning
• Mobile Signal Traffic Prediction
• Data Transmission Reduction

Innovation

• Gradient Compression
• Device Relationship Modeling
• Communication Efficiency Enhancement