Subgraph Federated Learning with Information Bottleneck Constrained Generative Learning

Federated Learning (FL) is a groundbreaking approach that enables multiple clients to jointly train deep learning models by pooling their data, while addressing privacy and bandwidth issues that prevent direct data sharing. This approach is particularly suitable for building strong and widely applicable graph models, given the increasing amounts of graph data stored across different locations. However, FL for subgraph models faces significant challenges, such as the diversity of data and the risk of attacks, which can affect the strength and reliability of these models. In response to these challenges, our research delves into the complexities of FL for subgraphs from an information theory perspective. We identify a major issue that affects the performance of graph models: the bias in the optimization goal of the commonly used FedAVG training method. To address this, we propose InfoFedGNN, an innovative FL framework for subgraphs that is based on the Information Bottleneck principle. InfoFedGNN is designed to overcome the problem of Non-Independent and Identically Distributed (non-i.i.d.) data in FL and to significantly improve its defense against security threats. Our thorough evaluation of InfoFedGNN on five public datasets, with both uniform and diverse data distributions, highlights its improved defense capabilities and better training outcomes. These results confirm the effectiveness of InfoFedGNN in enhancing the security and efficiency of FL, demonstrating its potential to push forward the development of federated graph models.