FedHGL: Cross-Institutional Federated Heterogeneous Graph Learning for IoT

Graph neural networks, effectively harnessing the extensive interactive data from Internet of Things (IoT) devices, significantly enhance service quality in IoT systems. However, traditional centralized training leads to the leakage of private data during the data collection and model training phases in IoT scenarios. Federated learning (FL) has emerged as a promising approach, facilitating collaborative model training across diverse IoT devices without sharing sensitive data. The intricate types and relationships among IoT devices from various institutions highlight the issues of class imbalance and graph heterogeneity across different clients. These issues decrease the performance of FL models. In this work, we focus on a more realistic scenario where the IoT institutions have only limited amount and types of data. We propose a cross-institutional federated heterogeneous graph learning method called FedHGL. It aims to mitigate the negative effects of class imbalance while maintaining the private data locally on clients during collaborative training. We employ a heterogeneous graph neural network as the training model for clients. FedHGL generates cross-client minority class samples to enhance the model performance. Additionally, it incorporates a compensation mechanism to prevent forgetting global information. FedHGL designs an adaptive aggregation coefficient that assigns weights to IoT institutions according to the class imbalance of their data, thereby optimizing the aggregation process. Extensive experiments demonstrate the effectiveness of FedHGL for class imbalance and heterogeneous graph data.