Saved in:
| Main Authors: | Zhu, Wanchuang, Zhao, Benjamin Zi Hao, Luo, Simon, Liu, Tongliang, Deng, Ke |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2110.11736 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Toward Malicious Clients Detection in Federated Learning
by: Dou, Zhihao, et al.
Published: (2025)
by: Dou, Zhihao, et al.
Published: (2025)
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning
by: Licciardi, Alessandro, et al.
Published: (2025)
by: Licciardi, Alessandro, et al.
Published: (2025)
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
by: Wu, Xinghao, et al.
Published: (2024)
by: Wu, Xinghao, et al.
Published: (2024)
Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
by: Wang, Fei, et al.
Published: (2025)
by: Wang, Fei, et al.
Published: (2025)
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics
by: Gao, Xianjun, et al.
Published: (2024)
by: Gao, Xianjun, et al.
Published: (2024)
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
by: Dai, Rong, et al.
Published: (2024)
by: Dai, Rong, et al.
Published: (2024)
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
by: Mozaffari, Hamid, et al.
Published: (2024)
by: Mozaffari, Hamid, et al.
Published: (2024)
Two trust region type algorithms for solving nonconvex-strongly concave minimax problems
by: Yao, Tongliang, et al.
Published: (2024)
by: Yao, Tongliang, et al.
Published: (2024)
Federated Learning with Limited Node Labels
by: Tang, Bisheng, et al.
Published: (2024)
by: Tang, Bisheng, et al.
Published: (2024)
Malicious Internet Entity Detection Using Local Graph Inference
by: Mandlik, Simon, et al.
Published: (2024)
by: Mandlik, Simon, et al.
Published: (2024)
What If the Input is Expanded in OOD Detection?
by: Zhang, Boxuan, et al.
Published: (2024)
by: Zhang, Boxuan, et al.
Published: (2024)
PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation
by: Liu, Yilun, et al.
Published: (2023)
by: Liu, Yilun, et al.
Published: (2023)
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning
by: Zhang, Boning, et al.
Published: (2024)
by: Zhang, Boning, et al.
Published: (2024)
Ranking-aware Reinforcement Learning for Ordinal Ranking
by: Hao, Aiming, et al.
Published: (2026)
by: Hao, Aiming, et al.
Published: (2026)
Federated Causal Discovery from Heterogeneous Data
by: Li, Loka, et al.
Published: (2024)
by: Li, Loka, et al.
Published: (2024)
Robust Training of Federated Models with Extremely Label Deficiency
by: Zhang, Yonggang, et al.
Published: (2024)
by: Zhang, Yonggang, et al.
Published: (2024)
Spectral Imbalance Causes Forgetting in Low-Rank Continual Adaptation
by: Gu, Hao, et al.
Published: (2026)
by: Gu, Hao, et al.
Published: (2026)
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
by: Liu, Ruyue, et al.
Published: (2024)
by: Liu, Ruyue, et al.
Published: (2024)
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense
by: Li, Qilei, et al.
Published: (2024)
by: Li, Qilei, et al.
Published: (2024)
Learning Robust Representations for Malicious Content Detection via Contrastive Sampling and Uncertainty Estimation
by: Hossain, Elias, et al.
Published: (2025)
by: Hossain, Elias, et al.
Published: (2025)
Privacy-Preserving Federated Learning via Dataset Distillation
by: Xu, ShiMao, et al.
Published: (2024)
by: Xu, ShiMao, et al.
Published: (2024)
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
by: Xhemrishi, Marvin, et al.
Published: (2023)
by: Xhemrishi, Marvin, et al.
Published: (2023)
Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences
by: Liu, Shudong, et al.
Published: (2026)
by: Liu, Shudong, et al.
Published: (2026)
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
by: Peng, Songtao, et al.
Published: (2025)
by: Peng, Songtao, et al.
Published: (2025)
Group-Adaptive Adversarial Learning for Robust Fake News Detection Against Malicious Comments
by: Tong, Zhao, et al.
Published: (2025)
by: Tong, Zhao, et al.
Published: (2025)
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA
by: Hao, Jie, et al.
Published: (2025)
by: Hao, Jie, et al.
Published: (2025)
Robust Node Affinities via Jaccard-Biased Random Walks and Rank Aggregation
by: Pfeifer, Bastian, et al.
Published: (2026)
by: Pfeifer, Bastian, et al.
Published: (2026)
Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning
by: Xu, Isaac, et al.
Published: (2026)
by: Xu, Isaac, et al.
Published: (2026)
One Node Per User: Node-Level Federated Learning for Graph Neural Networks
by: Gao, Zhidong, et al.
Published: (2024)
by: Gao, Zhidong, et al.
Published: (2024)
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
by: Li, Shikun, et al.
Published: (2023)
by: Li, Shikun, et al.
Published: (2023)
Efficient and Adaptable Detection of Malicious LLM Prompts via Bootstrap Aggregation
by: Hassan, Shayan Ali, et al.
Published: (2026)
by: Hassan, Shayan Ali, et al.
Published: (2026)
Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
by: Guo, Mingzhao, et al.
Published: (2024)
by: Guo, Mingzhao, et al.
Published: (2024)
Pruning and Malicious Injection: A Retraining-Free Backdoor Attack on Transformer Models
by: Zhao, Taibiao, et al.
Published: (2025)
by: Zhao, Taibiao, et al.
Published: (2025)
Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classification
by: Wang, Yancheng, et al.
Published: (2024)
by: Wang, Yancheng, et al.
Published: (2024)
GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum
by: Liu, Zekai, et al.
Published: (2024)
by: Liu, Zekai, et al.
Published: (2024)
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
by: Tian, Yuxin, et al.
Published: (2024)
by: Tian, Yuxin, et al.
Published: (2024)
Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services
by: Xing, Yue, et al.
Published: (2025)
by: Xing, Yue, et al.
Published: (2025)
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
FedGT: Federated Node Classification with Scalable Graph Transformer
by: Zhang, Zaixi, et al.
Published: (2024)
by: Zhang, Zaixi, et al.
Published: (2024)
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection
by: Cao, Chentao, et al.
Published: (2024)
by: Cao, Chentao, et al.
Published: (2024)
Similar Items
-
Toward Malicious Clients Detection in Federated Learning
by: Dou, Zhihao, et al.
Published: (2025) -
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning
by: Licciardi, Alessandro, et al.
Published: (2025) -
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
by: Wu, Xinghao, et al.
Published: (2024) -
Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
by: Wang, Fei, et al.
Published: (2025) -
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics
by: Gao, Xianjun, et al.
Published: (2024)