Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguardi...Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation;however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized Federated Graph Learning via Surrogate Model (SD_FGL). In SD_FGL, each client is required to maintain two models: a private model and a surrogate model. The surrogate model is publicly shared and can exchange and update information directly with any client, eliminating the need for a central server and reducing communication overhead. The private model is independently trained by each client, allowing it to calculate similarity with other clients based on local data as well as information shared through the surrogate model. This enables the private model to better adjust its training strategy and selectively update its parameters. Additionally, local differential privacy is incorporated into the surrogate model training process to enhance privacy protection. Testing on three real-world graph datasets demonstrates that the proposed framework improves accuracy while achieving decentralized Federated Graph Learning with lower communication overhead and stronger privacy safeguards.展开更多
Federated learning is a classic of privacy-preserving learning, which enables collaborative learning without sharing data. Structured data has become the mainstream of current applications, where there is a correlatio...Federated learning is a classic of privacy-preserving learning, which enables collaborative learning without sharing data. Structured data has become the mainstream of current applications, where there is a correlation between samples in the same dataset, which can be represented by a graph. Federated graph learning (FGL) is the integration of structured data into federated learning, assuming that each user has structured graph representation data and uses graph learning models for training. This article proposes an FGL algorithm based on graph distance calculation, which determines the similarity of users in terms of graph distance, then clusters users, and aggregates local models of users in the same cluster. This algorithm can adjust the number of clusters by changing the threshold (the larger the threshold, the fewer clusters and the more users in each cluster, and vice versa), thereby controlling the scope of user collaboration.展开更多
The increase in population and vehicles exacerbates traffic congestion and management difficulties.Therefore,achieving accurate and efficient traffic flow prediction is crucial for urban transportation.For that reason...The increase in population and vehicles exacerbates traffic congestion and management difficulties.Therefore,achieving accurate and efficient traffic flow prediction is crucial for urban transportation.For that reason,we propose a graph federated learning-based digital twin traffic flow prediction method(GFLDT)by integrating the benefits of collaborative intelligence and computation of intelligent IoT.Specifically,we construct a digital twin network for predicting traffic flow,which is divided into client twin and global twin.Based on this,we adopt the concept of graph federated learning to learn the temporal dependence of traffic flow using local data from client twins,and the spatial dependence of traffic flow using global information from global twins.In addition,we validate on a real traffic dataset,and the results show that through collaborative training of the client twins and the global twins,GFLDT achieves accurate traffic flow prediction while protecting data security.展开更多
Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,exi...Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.展开更多
基金supported by InnerMongolia Natural Science Foundation Project(2021LHMS06003)Inner Mongolia University Basic Research Business Fee Project(114).
文摘Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation;however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized Federated Graph Learning via Surrogate Model (SD_FGL). In SD_FGL, each client is required to maintain two models: a private model and a surrogate model. The surrogate model is publicly shared and can exchange and update information directly with any client, eliminating the need for a central server and reducing communication overhead. The private model is independently trained by each client, allowing it to calculate similarity with other clients based on local data as well as information shared through the surrogate model. This enables the private model to better adjust its training strategy and selectively update its parameters. Additionally, local differential privacy is incorporated into the surrogate model training process to enhance privacy protection. Testing on three real-world graph datasets demonstrates that the proposed framework improves accuracy while achieving decentralized Federated Graph Learning with lower communication overhead and stronger privacy safeguards.
文摘Federated learning is a classic of privacy-preserving learning, which enables collaborative learning without sharing data. Structured data has become the mainstream of current applications, where there is a correlation between samples in the same dataset, which can be represented by a graph. Federated graph learning (FGL) is the integration of structured data into federated learning, assuming that each user has structured graph representation data and uses graph learning models for training. This article proposes an FGL algorithm based on graph distance calculation, which determines the similarity of users in terms of graph distance, then clusters users, and aggregates local models of users in the same cluster. This algorithm can adjust the number of clusters by changing the threshold (the larger the threshold, the fewer clusters and the more users in each cluster, and vice versa), thereby controlling the scope of user collaboration.
基金supported by the National Natural Science Foundation of China(U23A20272,U22A2069,62272146)Natural Science Foundation of Henan(252300421237).
文摘The increase in population and vehicles exacerbates traffic congestion and management difficulties.Therefore,achieving accurate and efficient traffic flow prediction is crucial for urban transportation.For that reason,we propose a graph federated learning-based digital twin traffic flow prediction method(GFLDT)by integrating the benefits of collaborative intelligence and computation of intelligent IoT.Specifically,we construct a digital twin network for predicting traffic flow,which is divided into client twin and global twin.Based on this,we adopt the concept of graph federated learning to learn the temporal dependence of traffic flow using local data from client twins,and the spatial dependence of traffic flow using global information from global twins.In addition,we validate on a real traffic dataset,and the results show that through collaborative training of the client twins and the global twins,GFLDT achieves accurate traffic flow prediction while protecting data security.
基金supported by National Natural Science Foundation of China(62466045)Inner Mongolia Natural Science Foundation Project(2021LHMS06003)Inner Mongolia University Basic Research Business Fee Project(114).
文摘Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.
