Towards Federated Graph Learning in One-shot Communication

• URL: http://arxiv.org/abs/2411.11304v5
• Date: Thu, 23 Jan 2025 02:38:27 GMT
• Title: Towards Federated Graph Learning in One-shot Communication
• Authors: Guochen Yan, Xunkai Li, Luyuan Xie, Wentao Zhang, Qingni Shen, Yuejian Fang, Zhonghai Wu,
• Abstract summary: Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs.<n>One-shot Federated Learning (OFL) enables collaboration in a single round, but existing OFL methods are ineffective for graph data.<n>We propose the first $textbfO-pFGL$ method ($textbfO-pFGL$) for node classification, compatible with Secure Aggregation protocols for privacy preservation.
• Score: 27.325478113745206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distributed graph data, personalized Federated Graph Learning (pFGL) aims to enhance model utility by training personalized models tailored to client needs. However, existing pFGL methods often require numerous communication rounds under heterogeneous graphs, leading to significant communication overhead and security concerns. While One-shot Federated Learning (OFL) enables collaboration in a single round, existing OFL methods are designed for image-centric tasks and ineffective for graph data, leaving a critical gap in the field. Additionally, personalized models derived from existing methods suffer from bias, failing to effectively generalize to the minority. To address these challenges, we propose the first $\textbf{O}$ne-shot $\textbf{p}$ersonalized $\textbf{F}$ederated $\textbf{G}$raph $\textbf{L}$earning method ($\textbf{O-pFGL}$) for node classification, compatible with Secure Aggregation protocols for privacy preservation. Specifically, for effective graph learning in one communication round, our method estimates and aggregates class-wise feature distribution statistics to construct a global pseudo-graph on the server, facilitating the training of a global graph model. To mitigate bias, we introduce a two-stage personalized training approach that adaptively balances local personal information and global insights from the pseudo-graph, improving both personalization and generalization. Extensive experiments on 12 multi-scale graph datasets demonstrate that our method significantly outperforms state-of-the-art baselines across various settings.

Related papers

• FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs [55.51300642911766]
  Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner. FGL methods usually require that the graph data owned by all clients is homophilic to ensure similar neighbor distribution patterns of nodes. We propose FedHERO, an FGL framework designed to harness and share insights from heterophilic graphs effectively.
  arXiv  Detail & Related papers  (2025-04-29T22:23:35Z)
• Federated Prototype Graph Learning [33.38948169766356]
  Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities. FEMAIL: We propose FedPG as a general prototype-guided optimization method for the above multi-level FGL heterogeneity. Experiments demonstrate that FedPG outperforms SOTA baselines by an average of 3.57% in accuracy while reducing communication costs by 168x.
  arXiv  Detail & Related papers  (2025-04-13T09:21:21Z)
• Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition [18.99572321624751]
  We propose a communication-efficient personalized graph learning algorithm, CEFGL. Our method decomposes the model parameters into low-rank generic and sparse private models. We employ a dual-channel encoder to learn sparse local knowledge in a personalized manner.
  arXiv  Detail & Related papers  (2024-12-18T02:26:07Z)
• Personalized federated learning based on feature fusion [2.943623084019036]
  Federated learning enables distributed clients to collaborate on training while storing their data locally to protect client privacy. We propose a personalized federated learning approach called pFedPM. In our process, we replace traditional gradient uploading with feature uploading, which helps reduce communication costs and allows for heterogeneous client models.
  arXiv  Detail & Related papers  (2024-06-24T12:16:51Z)
• FedSheafHN: Personalized Federated Learning on Graph-structured Data [22.825083541211168]
  We propose a model called FedSheafHN, which embeds each client's local subgraph into a server-constructed collaboration graph. Our model improves the integration and interpretation of complex client characteristics. It also has fast model convergence and effective new clients generalization.
  arXiv  Detail & Related papers  (2024-05-25T04:51:41Z)
• MGNet: Learning Correspondences via Multiple Graphs [78.0117352211091]
  Learning correspondences aims to find correct correspondences from the initial correspondence set with an uneven correspondence distribution and a low inlier rate. Recent advances usually use graph neural networks (GNNs) to build a single type of graph or stack local graphs into the global one to complete the task. We propose MGNet to effectively combine multiple complementary graphs.
  arXiv  Detail & Related papers  (2024-01-10T07:58:44Z)
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks [72.01829954658889]
  This paper introduces the mathematical definition of this novel problem setting. We devise a general framework that coordinates a single graph-shared structure learner and multiple graph-specific GNNs. The well-trained structure learner can directly produce adaptive structures for unseen target graphs without any fine-tuning.
  arXiv  Detail & Related papers  (2023-06-20T03:33:22Z)
• Distributed Learning over Networks with Graph-Attention-Based Personalization [49.90052709285814]
  We propose a graph-based personalized algorithm (GATTA) for distributed deep learning. In particular, the personalized model in each agent is composed of a global part and a node-specific part. By treating each agent as one node in a graph the node-specific parameters as its features, the benefits of the graph attention mechanism can be inherited.
  arXiv  Detail & Related papers  (2023-05-22T13:48:30Z)
• Graph Learning Across Data Silos [12.343382413705394]
  We consider the problem of inferring graph topology from smooth graph signals in a novel but practical scenario. Data are located in distributed clients and prohibited from leaving local clients due to factors such as privacy concerns. We propose an auto-weighted multiple graph learning model to jointly learn a personalized graph for each local client and a single consensus graph for all clients.
  arXiv  Detail & Related papers  (2023-01-17T02:14:57Z)
• Contrastive Graph Few-Shot Learning [67.01464711379187]
  We propose a Contrastive Graph Few-shot Learning framework (CGFL) for graph mining tasks. CGFL learns data representation in a self-supervised manner, thus mitigating the distribution shift impact for better generalization. Comprehensive experiments demonstrate that CGFL outperforms state-of-the-art baselines on several graph mining tasks.
  arXiv  Detail & Related papers  (2022-09-30T20:40:23Z)
• FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs [22.649780281947837]
  In some practical scenarios, graph data are stored separately in multiple distributed parties, which may not be directly shared due to conflicts of interest. We propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above.
  arXiv  Detail & Related papers  (2022-08-29T15:47:36Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)
• Privatized Graph Federated Learning [57.14673504239551]
  We introduce graph federated learning, which consists of multiple units connected by a graph. We show how graph homomorphic perturbations can be used to ensure the algorithm is differentially private.
  arXiv  Detail & Related papers  (2022-03-14T13:48:23Z)
• Tackling the Local Bias in Federated Graph Learning [48.887310972708036]
  In Federated graph learning (FGL), a global graph is distributed across different clients, where each client holds a subgraph. Existing FGL methods fail to effectively utilize cross-client edges, losing structural information during the training. We propose a novel FGL framework to make the local models similar to the model trained in a centralized setting.
  arXiv  Detail & Related papers  (2021-10-22T08:22:36Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• FedGL: Federated Graph Learning Framework with Global Self-Supervision [22.124339267195822]
  FedGL is capable of obtaining a high-quality global graph model while protecting data privacy. The global self-supervision enables the information of each client to flow and share in a privacy-preserving manner.
  arXiv  Detail & Related papers  (2021-05-07T11:27:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.