Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
This addresses the trade-off between privacy and model performance in federated graph learning, which is crucial for real-world applications with sensitive data, though it is an incremental improvement.
The paper tackles the performance degradation of graph learning models when differential privacy (DP) is applied to graph edges in federated settings, and shows that using graph contrastive learning alleviates this drop, as demonstrated through experiments on four models across five benchmark datasets.
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on a single device is a typical approach for training a capable graph learning model. Due to privacy concerns, however, it is infeasible to do so in real-world scenarios. Federated learning provides a practical means of addressing this limitation by introducing various privacy-preserving mechanisms, such as differential privacy (DP) on the graph edges. However, although DP in federated graph learning can ensure the security of sensitive information represented in graphs, it usually causes the performance of graph learning models to degrade. In this paper, we investigate how DP can be implemented on graph edges and observe a performance decrease in our experiments. In addition, we note that DP on graph edges introduces noise that perturbs graph proximity, which is one of the graph augmentations in graph contrastive learning. Inspired by this, we propose leveraging graph contrastive learning to alleviate the performance drop resulting from DP. Extensive experiments conducted with four representative graph models on five widely used benchmark datasets show that contrastive learning indeed alleviates the models' DP-induced performance drops.