Imagen de Portada

FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yuan, Zhongzheng, Guo, Lianshuai, Li, Xunkai, Zhu, Yinlin, Wang, Wenyu, Qu, Meixia
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
por: Guo, Lianshuai, et al.
Publicado: (2025)

Generalized Category Discovery in Federated Graph Learning
por: Yuan, Zhongzheng, et al.
Publicado: (2026)

FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning
por: Zhou, Yanbing, et al.
Publicado: (2025)

FedGTA: Topology-aware Averaging for Federated Graph Learning
por: Li, Xunkai, et al.
Publicado: (2024)

Rethinking Federated Graph Learning: A Data Condensation Perspective
por: Zhang, Hao, et al.
Publicado: (2025)

Rethinking Client-oriented Federated Graph Learning
por: Chen, Zekai, et al.
Publicado: (2025)

FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
por: Zhu, Yinlin, et al.
Publicado: (2024)

Federated Prototype Graph Learning
por: Wu, Zhengyu, et al.
Publicado: (2025)

FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
por: Li, Yunbo, et al.
Publicado: (2025)

FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling
por: Wu, Zhengyu, et al.
Publicado: (2025)

CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
por: Zhang, Yu, et al.
Publicado: (2021)

Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
por: Zhu, Yinlin, et al.
Publicado: (2025)

FedAH: Aggregated Head for Personalized Federated Learning
por: Zhou, Pengzhan, et al.
Publicado: (2024)

Federated Continual Graph Learning
por: Zhu, Yinlin, et al.
Publicado: (2024)

FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
por: He, Yuepeng, et al.
Publicado: (2024)

CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning
por: Li, Yuecheng, et al.
Publicado: (2023)

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach
por: Zhu, Yinlin, et al.
Publicado: (2026)

Aggregation Design for Personalized Federated Multi-Modal Learning over Wireless Networks
por: Yin, Benshun, et al.
Publicado: (2024)

Federated Graph Unlearning
por: Ai, Yuming, et al.
Publicado: (2025)

CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation
por: Su, Daohan, et al.
Publicado: (2026)

Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
por: Wu, Zhengyu, et al.
Publicado: (2025)

An Experimental Study of Different Aggregation Schemes in Semi-Asynchronous Federated Learning
por: Li, Yunbo, et al.
Publicado: (2024)

TMTE: Effective Multimodal Graph Learning with Task-aware Modality and Topology Co-evolution
por: Zhu, Yinlin, et al.
Publicado: (2026)

Knowledge-Driven Federated Graph Learning on Model Heterogeneity
por: Wu, Zhengyu, et al.
Publicado: (2025)

SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
por: Islam, Md Sirajul, et al.
Publicado: (2025)

OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
por: Li, Xunkai, et al.
Publicado: (2024)

GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
por: Wang, Xu, et al.
Publicado: (2026)

FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
por: Ma, Jeffrey, et al.
Publicado: (2024)

FedAgg: Adaptive Federated Learning with Aggregated Gradients
por: Yuan, Wenhao, et al.
Publicado: (2023)

FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data
por: Chen, Yue, et al.
Publicado: (2025)

FedPSA: Modeling Behavioral Staleness in Asynchronous Federated Learning
por: Lu, Chaoyi, et al.
Publicado: (2026)

FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
por: Li, Youpeng, et al.
Publicado: (2024)

Personalized One-shot Federated Graph Learning for Heterogeneous Clients
por: Yan, Guochen, et al.
Publicado: (2024)

MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
por: Li, Xunkai, et al.
Publicado: (2026)

A Comprehensive Data-centric Overview of Federated Graph Learning
por: Wu, Zhengyu, et al.
Publicado: (2025)

GCL-GCN: Graphormer and Contrastive Learning Enhanced Attributed Graph Clustering Network
por: Li, Binxiong, et al.
Publicado: (2025)

Fed-PELAD: Communication-Efficient Federated Learning for Massive MIMO CSI Feedback with Personalized Encoders and a LoRA-Adapted Shared Decoder
por: Zhou, Yixiang, et al.
Publicado: (2025)

FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data
por: Sun, Yuxia, et al.
Publicado: (2025)

TopoGCL: Topological Graph Contrastive Learning
por: Chen, Yuzhou, et al.
Publicado: (2024)

FedIA: Towards Domain-Robust Aggregation in Federated Graph Learning
por: Zhou, Zhanting, et al.
Publicado: (2025)