Federated Prototype Graph Learning

• URL: http://arxiv.org/abs/2504.09493v1
• Date: Sun, 13 Apr 2025 09:21:21 GMT
• Title: Federated Prototype Graph Learning
• Authors: Zhengyu Wu, Xunkai Li, Yinlin Zhu, Rong-Hua Li, Guoren Wang, Chenghu Zhou,
• Abstract summary: Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities.<n>FEMAIL: We propose FedPG as a general prototype-guided optimization method for the above multi-level FGL heterogeneity.<n> Experiments demonstrate that FedPG outperforms SOTA baselines by an average of 3.57% in accuracy while reducing communication costs by 168x.
• Score: 33.38948169766356
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In recent years, Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities in graph-based machine intelligence applications, mitigating data silos while offering a new perspective for privacy-preserve large-scale graph learning. However, multi-level FGL heterogeneity presents various client-server collaboration challenges: (1) Model-level: The variation in clients for expected performance and scalability necessitates the deployment of heterogeneous models. Unfortunately, most FGL methods rigidly demand identical client models due to the direct model weight aggregation on the server. (2) Data-level: The intricate nature of graphs, marked by the entanglement of node profiles and topology, poses an optimization dilemma. This implies that models obtained by federated training struggle to achieve superior performance. (3) Communication-level: Some FGL methods attempt to increase message sharing among clients or between clients and the server to improve training, which inevitably leads to high communication costs. In this paper, we propose FedPG as a general prototype-guided optimization method for the above multi-level FGL heterogeneity. Specifically, on the client side, we integrate multi-level topology-aware prototypes to capture local graph semantics. Subsequently, on the server side, leveraging the uploaded prototypes, we employ topology-guided contrastive learning and personalized technology to tailor global prototypes for each client, broadcasting them to improve local training. Experiments demonstrate that FedPG outperforms SOTA baselines by an average of 3.57\% in accuracy while reducing communication costs by 168x.

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)
• Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach [15.747994926672868]
  Federated graph learning (FGL) has emerged as a promising paradigm for collaborative machine learning. MHtFGL problem arises in real-world applications, such as the aggregation of models from different companies. We propose the Federated Graph Knowledge Collaboration (FedGKC) framework, comprising two key components.
  arXiv  Detail & Related papers  (2025-01-22T04:12:32Z)
• 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)
• Towards Federated Graph Learning in One-shot Communication [27.325478113745206]
  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.
  arXiv  Detail & Related papers  (2024-11-18T05:59:29Z)
• SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation [16.599474223790843]
  Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for classification tasks. We propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration. We show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-07-14T09:34:19Z)
• 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)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• 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)
• An Expectation-Maximization Perspective on Federated Learning [75.67515842938299]
  Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting.
  arXiv  Detail & Related papers  (2021-11-19T12:58:59Z)
• Federated Multi-Target Domain Adaptation [99.93375364579484]
  Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. We consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We propose an effective DualAdapt method to address the new challenges.
  arXiv  Detail & Related papers  (2021-08-17T17:53:05Z)

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.