FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution
Graph Federated Learning (GFL) is an emerging distributed training paradigm that combines federated learning with graph data. Due to its ability to effectively handle complex and heterogeneous graph data while protecting user privacy, GFL has shown great potential in processing various types of grap...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10857280/ |
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| author | Hefei Wang Ruichun Gu Jingyu Wang Xiaolin Zhang Hui Wei |
| author_facet | Hefei Wang Ruichun Gu Jingyu Wang Xiaolin Zhang Hui Wei |
| author_sort | Hefei Wang |
| collection | DOAJ |
| description | Graph Federated Learning (GFL) is an emerging distributed training paradigm that combines federated learning with graph data. Due to its ability to effectively handle complex and heterogeneous graph data while protecting user privacy, GFL has shown great potential in processing various types of graph structures and has been proven effective in a wide range of applications. However, existing methods normally assign equal attention to all nodes within a single graph, focusing too much on the information of neighboring nodes, even if some nodes are more important in the graph structure or task (such as high consumption users or popular products), which inevitably leads to inefficient node embedding. To address this issue, this paper proposes an innovative graph federated learning framework called FedBFGCN (Graph Federated Learning Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution) to optimize the embedding and analysis efficiency of graph data. This proposed framework converts single graph data into node features and adjacency matrices for processing, and combines a customized Cross-Layer Feature Fusion Convolution(CLF) and an improved Attention Mechanism that is Balanced Channel Attention Mechanism (BCAM). The FedBFGCN improves the attention to important nodes by dynamically weighting and adjusting the weights of features through BCAM; Using CLF effectively integrates its own features with neighbor information, enhancing feature expression capability. Through the organic fusion of these two modules, the FedBFGCN achieves efficient, robust, and more comprehensive node embedding representation, demonstrating excellent performance in node classification and prediction tasks. In addition, this framework also uses homomorphic encryption methods to enhance privacy protection and improve data security. The FedBFGCN was evaluated on standard reference network datasets (Cora, Citeseer, Polblogs), and experimental results showed that it has lower losses and higher performance in multiple aspects. This framework is capable of addressing various challenges in graph federated learning, significantly improving learning effectiveness and application capabilities. This study not only provides new ideas for graph federated learning and GCN, but also demonstrates its enormous potential in practical applications. |
| format | Article |
| id | doaj-art-3a03d47de24c4f469c3cb7af7fe48621 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3a03d47de24c4f469c3cb7af7fe486212025-02-06T00:00:22ZengIEEEIEEE Access2169-35362025-01-0113219802199110.1109/ACCESS.2025.353600110857280FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion ConvolutionHefei Wang0https://orcid.org/0009-0003-8111-6442Ruichun Gu1Jingyu Wang2Xiaolin Zhang3Hui Wei4https://orcid.org/0009-0003-7537-0882School of Digital and Intelligence Industry, Inner Mongolia University of Science and Technology, Baotou, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science and Technology, Baotou, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science and Technology, Baotou, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science and Technology, Baotou, ChinaSchool of Digital and Intelligence Industry, Inner Mongolia University of Science and Technology, Baotou, ChinaGraph Federated Learning (GFL) is an emerging distributed training paradigm that combines federated learning with graph data. Due to its ability to effectively handle complex and heterogeneous graph data while protecting user privacy, GFL has shown great potential in processing various types of graph structures and has been proven effective in a wide range of applications. However, existing methods normally assign equal attention to all nodes within a single graph, focusing too much on the information of neighboring nodes, even if some nodes are more important in the graph structure or task (such as high consumption users or popular products), which inevitably leads to inefficient node embedding. To address this issue, this paper proposes an innovative graph federated learning framework called FedBFGCN (Graph Federated Learning Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution) to optimize the embedding and analysis efficiency of graph data. This proposed framework converts single graph data into node features and adjacency matrices for processing, and combines a customized Cross-Layer Feature Fusion Convolution(CLF) and an improved Attention Mechanism that is Balanced Channel Attention Mechanism (BCAM). The FedBFGCN improves the attention to important nodes by dynamically weighting and adjusting the weights of features through BCAM; Using CLF effectively integrates its own features with neighbor information, enhancing feature expression capability. Through the organic fusion of these two modules, the FedBFGCN achieves efficient, robust, and more comprehensive node embedding representation, demonstrating excellent performance in node classification and prediction tasks. In addition, this framework also uses homomorphic encryption methods to enhance privacy protection and improve data security. The FedBFGCN was evaluated on standard reference network datasets (Cora, Citeseer, Polblogs), and experimental results showed that it has lower losses and higher performance in multiple aspects. This framework is capable of addressing various challenges in graph federated learning, significantly improving learning effectiveness and application capabilities. This study not only provides new ideas for graph federated learning and GCN, but also demonstrates its enormous potential in practical applications.https://ieeexplore.ieee.org/document/10857280/Graph federated learningbalanced channel attention mechanismcross-layer feature fusion convolutionGCN |
| spellingShingle | Hefei Wang Ruichun Gu Jingyu Wang Xiaolin Zhang Hui Wei FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution IEEE Access Graph federated learning balanced channel attention mechanism cross-layer feature fusion convolution GCN |
| title | FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution |
| title_full | FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution |
| title_fullStr | FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution |
| title_full_unstemmed | FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution |
| title_short | FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution |
| title_sort | fedbfgcn a graph federated learning framework based on balanced channel attention and cross layer feature fusion convolution |
| topic | Graph federated learning balanced channel attention mechanism cross-layer feature fusion convolution GCN |
| url | https://ieeexplore.ieee.org/document/10857280/ |
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