Fedgraphnet: a federated graph neural network framework for privacy-preserving traffic forecasting in heterogeneous IOT networks

Abstract

However, in fifth-generation (5G) and beyond networks, where mobile systems support non-communication applications and act as heterogeneous Internet of Things (IoT) environments, accurate forecasting is essential for proactive resource management, network slicing optimisation, and Quality of Service (QoS) assurance. However, the distributed and privacy-sensitive nature of IoT data limits centralised learning approaches. This paper proposes FedGraphNet, a federated learning (FL) framework integrating a spatio-temporal graph neural network (ST-GNN) with differential privacy (DP) for collaborative traffic prediction without sharing raw data among distributed IoT nodes. FedGraphNet introduces the Adaptive Graph Attention Aggregation (AGAA) module to dynamically construct adjacency matrices from partial network observations, addressing structural heterogeneity in real-world IoT deployments. A communication-efficient TopK-SVD gradient compression strategy reduces uplink overhead by 68.4% with less than 1.2% accuracy loss. A calibrated Gaussian mechanism ensures (ε=1.0, δ=10-5)-differential privacy during aggregation. Experiments on TaxiBJ21, Metr-LA, and PEMS-BAY datasets show that FedGraphNet reduces Mean Absolute Error (MAE) by 2.84, 3.11, and 1.96 respectively compared with six baselines, including FedAvg-GCN, Diffusion Convolutional Recurrent Neural Network (DCRNN), and Graph-WaveNet. The framework also reduces communication cost by 3.1× and accelerates convergence by 54.4% over FedAvg-GCN. Notably, FedGraphNet with ε=1.0 DP outperforms the FedGNN baseline without privacy protection, indicating that calibrated noise injection can serve as an effective regulariser for non-independent and identically distributed (non-IID) IoT traffic distributions. These results demonstrate the trade-offs among spatio-temporal accuracy, communication efficiency, and formal privacy, validating FedGraphNet as a deployable solution for next-generation 5G IoT network management.