FedGL: Federated Graph Learning Framework with Global Self-Supervision
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningGraph LearningPrivacy PreservingPseudo LabelSimilar Papers 제목 키워드 기반
Federated Graph Learning with Graphless Clients
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing methods usually assume that each client h…
Graph LearningKnowledge DistillationTransfer LearningBeyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
Federated learning (FL) enables collaborative model training with privacy preservation. Data heterogeneity across edge devices (clients) can cause models to converge to sharp minima, negatively impacting generalization a…
Federated LearningHide and Find: A Distributed Adversarial Attack on Federated Graph Learning
Federated Graph Learning (FedGL) is vulnerable to malicious attacks, yet developing a truly effective and stealthy attack method remains a significant challenge. Existing attack methods suffer from low attack success rat…
Federated LearningAdversarial AttackGraph LearningFaster Non-Convex Federated Learning via Global and Local Momentum
We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of $\mathcal{O}(\epsilon^{-1.5})$ to converge to an $\epsilon$-stationary point (i.e., $\mathbb{E}[\|\nabla f(\bm{x})\|…
Federated LearningFedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions
Continuous glucose monitoring (CGM) devices provide real-time glucose monitoring and timely alerts for glycemic excursions, improving glycemic control among patients with diabetes. However, identifying rare events like h…
Federated LearningPersonalized Federated Learning