Knowledge-Sharing Personalized Federated Subgraph Learning for Internet of Unmanned Agents

By integrating subgraph learning with federated learning, federated subgraph learning realizes collaborative learning of subgraph information among distributed Unmanned Agents (UAs) while protecting data privacy, offering a promising solution for graph modeling in Internet of Unmanned Agents (IUA). However, due to the various manners of collecting data on different UAs, graph data exhibits the features of Non-Independent and Identically Distributed (Non-IID), while the structures and features of local graph data on UAs are quite diverse. These factors lead to convergence difficulties and insufficient generalization ability of federated subgraph learning during the training process. To address these important challenges, we propose PFedSL, a novel knowledge-sharing Personalized Federated Subgraph Learning framework for IUA. First, a new personalized model aggregation is performed based on the confidence score of UAs and their similarity to reduce the interference of Non-IID data on model performance. Next, a parameter selective activation is introduced for model updating to handle the heterogeneity issue of subgraph structural features. Finally, an original personalized single-view contrastive learning is designed to optimize node embedding, thereby enhancing local representation consistency. Using real-world benchmark graph datasets, extensive experiments demonstrate the superiority of the proposed PFedSL. The results show that PFedSL achieves higher node classification accuracy than state-of-the-art methods in different scenarios. Meanwhile, the effectiveness of the core components in PFedSL is validated via ablation studies.