Federated Cross-Client Subgraph Pattern Detection
Fuente:
arXiv
Guardado en:
| Autores principales: | Ceydeli, Selin, Wang, Rui, Atasu, Kubilay |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection
por: Blanuša, Jovan, et al.
Publicado: (2024)
por: Blanuša, Jovan, et al.
Publicado: (2024)
Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations
por: Bilgi, H. Çağrı, et al.
Publicado: (2024)
por: Bilgi, H. Çağrı, et al.
Publicado: (2024)
Provably Powerful Graph Neural Networks for Directed Multigraphs
por: Egressy, Béni, et al.
Publicado: (2023)
por: Egressy, Béni, et al.
Publicado: (2023)
Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
por: Altman, Erik, et al.
Publicado: (2023)
por: Altman, Erik, et al.
Publicado: (2023)
Personalized Subgraph Federated Learning with Sheaf Collaboration
por: Liang, Wenfei, et al.
Publicado: (2025)
por: Liang, Wenfei, et al.
Publicado: (2025)
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
por: Xu, Jiahao, et al.
Publicado: (2025)
por: Xu, Jiahao, et al.
Publicado: (2025)
FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
por: Wang, Xinpeng, et al.
Publicado: (2024)
por: Wang, Xinpeng, et al.
Publicado: (2024)
Decoupled Subgraph Federated Learning
por: Aliakbari, Javad, et al.
Publicado: (2024)
por: Aliakbari, Javad, et al.
Publicado: (2024)
Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing
por: Guo, Yongxin, et al.
Publicado: (2024)
por: Guo, Yongxin, et al.
Publicado: (2024)
Toward Malicious Clients Detection in Federated Learning
por: Dou, Zhihao, et al.
Publicado: (2025)
por: Dou, Zhihao, et al.
Publicado: (2025)
Anomalous Client Detection in Federated Learning
por: Thakur, Dipanwita, et al.
Publicado: (2024)
por: Thakur, Dipanwita, et al.
Publicado: (2024)
Rethinking Client-oriented Federated Graph Learning
por: Chen, Zekai, et al.
Publicado: (2025)
por: Chen, Zekai, et al.
Publicado: (2025)
Subgraph Federated Learning for Local Generalization
por: Kim, Sungwon, et al.
Publicado: (2025)
por: Kim, Sungwon, et al.
Publicado: (2025)
Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks
por: Li, Kunhao, et al.
Publicado: (2025)
por: Li, Kunhao, et al.
Publicado: (2025)
Curriculum Guided Personalized Subgraph Federated Learning
por: Kang, Minku, et al.
Publicado: (2025)
por: Kang, Minku, et al.
Publicado: (2025)
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning
por: Licciardi, Alessandro, et al.
Publicado: (2025)
por: Licciardi, Alessandro, et al.
Publicado: (2025)
Federated Linear Contextual Bandits with Heterogeneous Clients
por: Blaser, Ethan, et al.
Publicado: (2024)
por: Blaser, Ethan, et al.
Publicado: (2024)
Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
por: Pérez-Corral, Cristian, et al.
Publicado: (2026)
por: Pérez-Corral, Cristian, et al.
Publicado: (2026)
Client Contribution Normalization for Enhanced Federated Learning
por: Kundalwal, Mayank Kumar, et al.
Publicado: (2024)
por: Kundalwal, Mayank Kumar, et al.
Publicado: (2024)
Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning
por: Udsa, Tinnakit, et al.
Publicado: (2025)
por: Udsa, Tinnakit, et al.
Publicado: (2025)
Federated Learning with Extremely Noisy Clients via Negative Distillation
por: Lu, Yang, et al.
Publicado: (2023)
por: Lu, Yang, et al.
Publicado: (2023)
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
por: Zhuo, Wei, et al.
Publicado: (2025)
por: Zhuo, Wei, et al.
Publicado: (2025)
Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework
por: He, Guanxiong, et al.
Publicado: (2025)
por: He, Guanxiong, et al.
Publicado: (2025)
Federated Graph Learning with Graphless Clients
por: Fu, Xingbo, et al.
Publicado: (2024)
por: Fu, Xingbo, et al.
Publicado: (2024)
Distributionally Robust Federated Learning with Client Drift Minimization
por: Krouka, Mounssif, et al.
Publicado: (2025)
por: Krouka, Mounssif, et al.
Publicado: (2025)
Client Selection for Federated Policy Optimization with Environment Heterogeneity
por: Xie, Zhijie, et al.
Publicado: (2023)
por: Xie, Zhijie, et al.
Publicado: (2023)
LEFL: Low Entropy Client Sampling in Federated Learning
por: Abebe, Waqwoya, et al.
Publicado: (2023)
por: Abebe, Waqwoya, et al.
Publicado: (2023)
Communication-Efficient Federated Learning With Data and Client Heterogeneity
por: Zakerinia, Hossein, et al.
Publicado: (2022)
por: Zakerinia, Hossein, et al.
Publicado: (2022)
Optimal Strategies for Federated Learning Maintaining Client Privacy
por: Bhaskar, Uday, et al.
Publicado: (2025)
por: Bhaskar, Uday, et al.
Publicado: (2025)
Who to Trust? Aggregating Client Predictions in Federated Distillation
por: Kovalchuk, Viktor, et al.
Publicado: (2025)
por: Kovalchuk, Viktor, et al.
Publicado: (2025)
Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation
por: Li, Yiwei, et al.
Publicado: (2025)
por: Li, Yiwei, et al.
Publicado: (2025)
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance
por: Zeng, Dun, et al.
Publicado: (2023)
por: Zeng, Dun, et al.
Publicado: (2023)
Subgraph Federated Learning via Spectral Methods
por: Aliakbari, Javad, et al.
Publicado: (2025)
por: Aliakbari, Javad, et al.
Publicado: (2025)
Balancing Client Participation in Federated Learning Using AoI
por: Javani, Alireza, et al.
Publicado: (2025)
por: Javani, Alireza, et al.
Publicado: (2025)
SLVR: Securely Leveraging Client Validation for Robust Federated Learning
por: Choi, Jihye, et al.
Publicado: (2025)
por: Choi, Jihye, et al.
Publicado: (2025)
Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case
por: Marfo, William, et al.
Publicado: (2025)
por: Marfo, William, et al.
Publicado: (2025)
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
por: Fraboni, Yann, et al.
Publicado: (2022)
por: Fraboni, Yann, et al.
Publicado: (2022)
Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models
por: Wang, Zezhou, et al.
Publicado: (2024)
por: Wang, Zezhou, et al.
Publicado: (2024)
FedOUI: OUI-Guided Client Weighting for Federated Aggregation
por: Fernández-Hernández, Alberto, et al.
Publicado: (2026)
por: Fernández-Hernández, Alberto, et al.
Publicado: (2026)
Personalized One-shot Federated Graph Learning for Heterogeneous Clients
por: Yan, Guochen, et al.
Publicado: (2024)
por: Yan, Guochen, et al.
Publicado: (2024)
Ejemplares similares
-
Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection
por: Blanuša, Jovan, et al.
Publicado: (2024) -
Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations
por: Bilgi, H. Çağrı, et al.
Publicado: (2024) -
Provably Powerful Graph Neural Networks for Directed Multigraphs
por: Egressy, Béni, et al.
Publicado: (2023) -
Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
por: Altman, Erik, et al.
Publicado: (2023) -
Personalized Subgraph Federated Learning with Sheaf Collaboration
por: Liang, Wenfei, et al.
Publicado: (2025)