Saved in:
| Main Authors: | Yuan, Zhongzheng, Guo, Lianshuai, Li, Xunkai, Zhu, Yinlin, Wang, Wenyu, Qu, Meixia |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.18219 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
by: Guo, Lianshuai, et al.
Published: (2025)
by: Guo, Lianshuai, 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)
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning
by: Zhou, Yanbing, et al.
Published: (2025)
by: Zhou, Yanbing, et al.
Published: (2025)
FedGTA: Topology-aware Averaging for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, 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)
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)
Federated Prototype Graph Learning
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
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)
FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
by: Li, Yunbo, et al.
Published: (2025)
by: Li, Yunbo, 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)
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)
Federated Graph Unlearning
by: Ai, Yuming, et al.
Published: (2025)
by: Ai, Yuming, et al.
Published: (2025)
Federated Continual Graph Learning
by: Zhu, Yinlin, et al.
Published: (2024)
by: Zhu, Yinlin, et al.
Published: (2024)
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
by: Zhang, Yu, et al.
Published: (2021)
by: Zhang, Yu, et al.
Published: (2021)
FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)
by: Zhou, Pengzhan, et al.
Published: (2024)
CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation
by: Su, Daohan, et al.
Published: (2026)
by: Su, Daohan, et al.
Published: (2026)
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
by: He, Yuepeng, et al.
Published: (2024)
by: He, Yuepeng, et al.
Published: (2024)
Aggregation Design for Personalized Federated Multi-Modal Learning over Wireless Networks
by: Yin, Benshun, et al.
Published: (2024)
by: Yin, Benshun, et al.
Published: (2024)
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning
by: Li, Yuecheng, et al.
Published: (2023)
by: Li, Yuecheng, et al.
Published: (2023)
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)
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
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)
MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2026)
by: Li, Xunkai, et al.
Published: (2026)
An Experimental Study of Different Aggregation Schemes in Semi-Asynchronous Federated Learning
by: Li, Yunbo, et al.
Published: (2024)
by: Li, Yunbo, 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)
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
by: Islam, Md Sirajul, et al.
Published: (2025)
by: Islam, Md Sirajul, et al.
Published: (2025)
Personalized One-shot Federated Graph Learning for Heterogeneous Clients
by: Yan, Guochen, et al.
Published: (2024)
by: Yan, Guochen, et al.
Published: (2024)
FedAgg: Adaptive Federated Learning with Aggregated Gradients
by: Yuan, Wenhao, et al.
Published: (2023)
by: Yuan, Wenhao, et al.
Published: (2023)
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
by: Ma, Jeffrey, et al.
Published: (2024)
by: Ma, Jeffrey, et al.
Published: (2024)
FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
by: Lu, Chaoyi, et al.
Published: (2026)
by: Lu, Chaoyi, et al.
Published: (2026)
FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data
by: Chen, Yue, et al.
Published: (2025)
by: Chen, Yue, et al.
Published: (2025)
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
by: Li, Youpeng, et al.
Published: (2024)
by: Li, Youpeng, et al.
Published: (2024)
GCL-GCN: Graphormer and Contrastive Learning Enhanced Attributed Graph Clustering Network
by: Li, Binxiong, et al.
Published: (2025)
by: Li, Binxiong, et al.
Published: (2025)
Fed-PELAD: Communication-Efficient Federated Learning for Massive MIMO CSI Feedback with Personalized Encoders and a LoRA-Adapted Shared Decoder
by: Zhou, Yixiang, et al.
Published: (2025)
by: Zhou, Yixiang, et al.
Published: (2025)
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data
by: Sun, Yuxia, et al.
Published: (2025)
by: Sun, Yuxia, et al.
Published: (2025)
TopoGCL: Topological Graph Contrastive Learning
by: Chen, Yuzhou, et al.
Published: (2024)
by: Chen, Yuzhou, et al.
Published: (2024)
FedIA: Towards Domain-Robust Aggregation in Federated Graph Learning
by: Zhou, Zhanting, et al.
Published: (2025)
by: Zhou, Zhanting, et al.
Published: (2025)
Similar Items
-
DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
by: Guo, Lianshuai, et al.
Published: (2025) -
Generalized Category Discovery in Federated Graph Learning
by: Yuan, Zhongzheng, et al.
Published: (2026) -
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning
by: Zhou, Yanbing, et al.
Published: (2025) -
FedGTA: Topology-aware Averaging for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024) -
Rethinking Federated Graph Learning: A Data Condensation Perspective
by: Zhang, Hao, et al.
Published: (2025)