Saved in:
| Main Authors: | Guo, Lianshuai, Yuan, Zhongzheng, Li, Xunkai, Zhu, Yinlin, Qu, Meixia, Wang, Wenyu |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11530 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
by: Yuan, Zhongzheng, et al.
Published: (2025)
by: Yuan, Zhongzheng, et al.
Published: (2025)
Generalized Category Discovery in Federated Graph Learning
by: Yuan, Zhongzheng, et al.
Published: (2026)
by: Yuan, Zhongzheng, et al.
Published: (2026)
FedGTA: Topology-aware Averaging for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
by: Zhu, Yinlin, et al.
Published: (2026)
by: Zhu, Yinlin, et al.
Published: (2026)
Rethinking Client-oriented Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2025)
by: Chen, Zekai, et al.
Published: (2025)
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
by: Zhu, Yinlin, et al.
Published: (2024)
by: Zhu, Yinlin, et al.
Published: (2024)
Rethinking Federated Graph Learning: A Data Condensation Perspective
by: Zhang, Hao, et al.
Published: (2025)
by: Zhang, Hao, et al.
Published: (2025)
Federated Prototype Graph Learning
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
by: Zhang, Kaichuang, et al.
Published: (2025)
by: Zhang, Kaichuang, et al.
Published: (2025)
Federated Graph Unlearning
by: Ai, Yuming, et al.
Published: (2025)
by: Ai, Yuming, et al.
Published: (2025)
GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
by: Wang, Xu, et al.
Published: (2026)
by: Wang, Xu, et al.
Published: (2026)
Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach
by: Zhu, Yinlin, et al.
Published: (2026)
by: Zhu, Yinlin, et al.
Published: (2026)
FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
by: Zhu, Yinlin, et al.
Published: (2025)
by: Zhu, Yinlin, et al.
Published: (2025)
MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2026)
by: Li, Xunkai, et al.
Published: (2026)
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2026)
by: Chen, Zekai, et al.
Published: (2026)
Federated Continual Graph Learning
by: Zhu, Yinlin, et al.
Published: (2024)
by: Zhu, Yinlin, et al.
Published: (2024)
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
A Comprehensive Data-centric Overview of Federated Graph Learning
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
TIP: Resisting Gradient Inversion via Targeted Interpretable Perturbation in Federated Learning
by: Wang, Jianhua, et al.
Published: (2026)
by: Wang, Jianhua, et al.
Published: (2026)
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
by: Pan, Qiying, et al.
Published: (2023)
by: Pan, Qiying, et al.
Published: (2023)
Topology-aware Generalization of Decentralized SGD
by: Zhu, Tongtian, et al.
Published: (2022)
by: Zhu, Tongtian, et al.
Published: (2022)
Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs
by: Zhu, Yinlin, et al.
Published: (2026)
by: Zhu, Yinlin, et al.
Published: (2026)
Personalized One-shot Federated Graph Learning for Heterogeneous Clients
by: Yan, Guochen, et al.
Published: (2024)
by: Yan, Guochen, et al.
Published: (2024)
Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
by: Wu, Xiang, et al.
Published: (2026)
by: Wu, Xiang, et al.
Published: (2026)
Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
by: Zhang, Sirui, et al.
Published: (2026)
by: Zhang, Sirui, et al.
Published: (2026)
Sharpness-aware Federated Graph Learning
by: Li, Ruiyu, et al.
Published: (2025)
by: Li, Ruiyu, et al.
Published: (2025)
OptiMAG: Structure-Semantic Alignment via Unbalanced Optimal Transport
by: Zuo, Yilong, et al.
Published: (2026)
by: Zuo, Yilong, et al.
Published: (2026)
BoostFGL: Boosting Fairness in Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2026)
by: Chen, Zekai, et al.
Published: (2026)
Unlocking Graph Structure Learning with Tree-Guided Large Language Models
by: Zhang, Zhihan, et al.
Published: (2025)
by: Zhang, Zhihan, et al.
Published: (2025)
Federated Graph Semantic and Structural Learning
by: Huang, Wenke, et al.
Published: (2024)
by: Huang, Wenke, et al.
Published: (2024)
RoleMAG: Learning Neighbor Roles in Multimodal Graphs
by: Zuo, Yilong, et al.
Published: (2026)
by: Zuo, Yilong, et al.
Published: (2026)
Adaptive Decentralized Federated Learning for Robust Optimization
by: Wu, Shuyuan, et al.
Published: (2025)
by: Wu, Shuyuan, et al.
Published: (2025)
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphs
by: Chen, Zekai, et al.
Published: (2026)
by: Chen, Zekai, et al.
Published: (2026)
DNNLasso: Scalable Graph Learning for Matrix-Variate Data
by: Lin, Meixia, et al.
Published: (2024)
by: Lin, Meixia, et al.
Published: (2024)
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning
by: Wu, Xiang, et al.
Published: (2025)
by: Wu, Xiang, et al.
Published: (2025)
Equipping Federated Graph Neural Networks with Structure-aware Group Fairness
by: Cui, Nan, et al.
Published: (2023)
by: Cui, Nan, et al.
Published: (2023)
Similar Items
-
FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
by: Yuan, Zhongzheng, et al.
Published: (2025) -
Generalized Category Discovery in Federated Graph Learning
by: Yuan, Zhongzheng, et al.
Published: (2026) -
FedGTA: Topology-aware Averaging for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024) -
TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
by: Zhu, Yinlin, et al.
Published: (2026) -
Rethinking Client-oriented Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2025)