SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

• URL: http://arxiv.org/abs/2407.11085v1
• Date: Sun, 14 Jul 2024 09:34:19 GMT
• Title: SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation
• Authors: Luying Zhong, Yueyang Pi, Zheyi Chen, Zhengxin Yu, Wang Miao, Xing Chen, Geyong Min,
• Abstract summary: 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.
• Score: 16.599474223790843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

Related papers

• 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)
• Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients [30.135431295658343]
  Federated learning (FL) aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients. In this paper, we propose an efficient federated learning (AFL) framework called DeFedAvg. DeFedAvg is the first AFL algorithm that achieves the desirable linear speedup property, which indicates its high scalability.
  arXiv  Detail & Related papers  (2024-02-17T05:22:46Z)
• FLASH: Federated Learning Across Simultaneous Heterogeneities [54.80435317208111]
  FLASH(Federated Learning Across Simultaneous Heterogeneities) is a lightweight and flexible client selection algorithm. It outperforms state-of-the-art FL frameworks under extensive sources of Heterogeneities. It achieves substantial and consistent improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-13T20:04:39Z)
• FedGTA: Topology-aware Averaging for Federated Graph Learning [44.11777886421429]
  Federated Graph Learning (FGL) is a distributed machine learning paradigm that enables collaborative training on large-scale subgraphs. Most FGL optimization strategies ignore graph structure, presenting dissatisfied performance and slow convergence. We propose Federated Graph Topology-aware Aggregation (FedGTA), a personalized optimization strategy that optimize through topology-aware local smoothing confidence and mixed neighbor features.
  arXiv  Detail & Related papers  (2024-01-22T08:31:53Z)
• AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity [44.11777886421429]
  Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks. We introduce the concept of structure Non-iid split and then present a new paradigm called underlineAdaptive underlineFederated underlineGraph underlineLearning (AdaFGL) Our proposed AdaFGL outperforms baselines by significant margins of 3.24% and 5.57% on community split and structure Non-iid split, respectively.
  arXiv  Detail & Related papers  (2024-01-22T08:23:31Z)
• Chasing Fairness in Graphs: A GNN Architecture Perspective [73.43111851492593]
  We propose textsfFair textsfMessage textsfPassing (FMP) designed within a unified optimization framework for graph neural networks (GNNs) In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets.
  arXiv  Detail & Related papers  (2023-12-19T18:00:15Z)
• 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)
• 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)
• Towards Fair Federated Learning with Zero-Shot Data Augmentation [123.37082242750866]
  Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data. We propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server).
  arXiv  Detail & Related papers  (2021-04-27T18:23:54Z)
• Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability [46.85205907718874]
  Federated learning is a new machine learning framework, where a bunch of clients collaboratively train a model without sharing training data. In this work, we consider a practical and issue when deploying federated learning in intermittent mobile environments. We propose a simple distributed nonlinear optimization algorithm, called Federated Latest Averaging (FedLaAvg for short) Our theoretical analysis shows that FedLaAvg attains the convergence rate of $(E1/2/(NT1/2)$, achieving a sublinear speed with respect to the total number of clients.
  arXiv  Detail & Related papers  (2020-02-18T06:32:18Z)

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.