FedGL: Federated Graph Learning Framework with Global Self-Supervision
arXiv:2105.03170
Abstract
Graph data are ubiquitous in the real world. Graph learning (GL) tries to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are designed for centralized scenarios. However, in practical scenarios, graph data are usually distributed in different organizations, i.e., the curse of isolated data islands. To address this problem, we incorporate federated learning into GL and propose a general Federated Graph Learning framework FedGL, which is capable of obtaining a high-quality global graph model while protecting data privacy by discovering the global self-supervision information during the federated training. Concretely, we propose to upload the prediction results and node embeddings to the server for discovering the global pseudo label and global pseudo graph, which are distributed to each client to enrich the training labels and complement the graph structure respectively, thereby improving the quality of each local model. Moreover, the global self-supervision enables the information of each client to flow and share in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients. Finally, experimental results show that FedGL significantly outperforms baselines on four widely used graph datasets.
References in corpus (22)
- Community detection in graphs
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
- Inductive Representation Learning on Large Graphs
- Federated Learning: Strategies for Improving Communication Efficiency
- Spectral Networks and Locally Connected Networks on Graphs
- Deep Convolutional Networks on Graph-Structured Data
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- Representation Learning on Graphs with Jumping Knowledge Networks
- Variational Graph Auto-Encoders
- How To Backdoor Federated Learning
- A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
- Secure Federated Transfer Learning
- Towards Deeper Graph Neural Networks
- FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
- Federated Meta-Learning with Fast Convergence and Efficient Communication
- Attention-based Graph Neural Network for Semi-supervised Learning
- Federated Multi-Task Learning
- Federated Deep Reinforcement Learning
- Adversarially Regularized Graph Autoencoder for Graph Embedding
- GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
- Client-Edge-Cloud Hierarchical Federated Learning