How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?
Fuente:
arXiv
Guardado en:
| Autores principales: | Ding, Wenjun, An, Ying, Chen, Lixing, Kan, Shichao, Wu, Fan, Qu, Zhe |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A Closer Look at Adversarial Suffix Learning for Jailbreaking LLMs: Augmented Adversarial Trigger Learning
por: Wang, Zhe, et al.
Publicado: (2025)
por: Wang, Zhe, et al.
Publicado: (2025)
Domain Adversarial Active Learning for Domain Generalization Classification
por: Chen, Jianting, et al.
Publicado: (2024)
por: Chen, Jianting, et al.
Publicado: (2024)
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks
por: Yang, Guangrui, et al.
Publicado: (2024)
por: Yang, Guangrui, et al.
Publicado: (2024)
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
por: Soltany, Milad, et al.
Publicado: (2024)
por: Soltany, Milad, et al.
Publicado: (2024)
Generalized Category Discovery in Federated Graph Learning
por: Yuan, Zhongzheng, et al.
Publicado: (2026)
por: Yuan, Zhongzheng, et al.
Publicado: (2026)
FedModule: A Modular Federated Learning Framework
por: Chen, Chuyi, et al.
Publicado: (2024)
por: Chen, Chuyi, et al.
Publicado: (2024)
Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing
por: Zhong, Xuyang, et al.
Publicado: (2025)
por: Zhong, Xuyang, et al.
Publicado: (2025)
FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning
por: Tang, Minxue, et al.
Publicado: (2024)
por: Tang, Minxue, et al.
Publicado: (2024)
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
por: Fan, Boyu, et al.
Publicado: (2024)
por: Fan, Boyu, et al.
Publicado: (2024)
Contrastive Regularization over LoRA for Multimodal Biomedical Image Incremental Learning
por: Zhang, Haojie, et al.
Publicado: (2025)
por: Zhang, Haojie, et al.
Publicado: (2025)
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
por: Ganesh, Swetha, et al.
Publicado: (2024)
por: Ganesh, Swetha, et al.
Publicado: (2024)
The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
por: Liu, Shuze Daniel, et al.
Publicado: (2024)
por: Liu, Shuze Daniel, et al.
Publicado: (2024)
FAA-CLIP: Federated Adversarial Adaptation of CLIP
por: Wu, Yihang, et al.
Publicado: (2025)
por: Wu, Yihang, et al.
Publicado: (2025)
HealSplit: Towards Self-Healing through Adversarial Distillation in Split Federated Learning
por: Xie, Yuhan, et al.
Publicado: (2025)
por: Xie, Yuhan, et al.
Publicado: (2025)
How Smoothing is N-simplicial Attention?
por: Dussolle, Alexandre, et al.
Publicado: (2025)
por: Dussolle, Alexandre, et al.
Publicado: (2025)
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning
por: Lai, Jiahao, et al.
Publicado: (2024)
por: Lai, Jiahao, et al.
Publicado: (2024)
Certified Adversarial Robustness via Partition-based Randomized Smoothing
por: Goli, Hossein, et al.
Publicado: (2024)
por: Goli, Hossein, et al.
Publicado: (2024)
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
por: Wu, Qiong, et al.
Publicado: (2024)
por: Wu, Qiong, et al.
Publicado: (2024)
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
por: Kim, Taejin, et al.
Publicado: (2023)
por: Kim, Taejin, et al.
Publicado: (2023)
Grounding Foundation Models through Federated Transfer Learning: A General Framework
por: Kang, Yan, et al.
Publicado: (2023)
por: Kang, Yan, et al.
Publicado: (2023)
Exploring Sparsity and Smoothness of Arbitrary $\ell_p$ Norms in Adversarial Attacks
por: Duhme, Christof, et al.
Publicado: (2026)
por: Duhme, Christof, et al.
Publicado: (2026)
Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood
por: Yao, Qingmao, et al.
Publicado: (2025)
por: Yao, Qingmao, et al.
Publicado: (2025)
Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives
por: Liu, Wei, et al.
Publicado: (2026)
por: Liu, Wei, et al.
Publicado: (2026)
Hide and Find: A Distributed Adversarial Attack on Federated Graph Learning
por: Liu, Jinshan, et al.
Publicado: (2026)
por: Liu, Jinshan, et al.
Publicado: (2026)
Principled Approximation Methods for Efficient and Scalable Deep Learning
por: Savarese, Pedro
Publicado: (2025)
por: Savarese, Pedro
Publicado: (2025)
Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization
por: Zhang, Xinyu, et al.
Publicado: (2023)
por: Zhang, Xinyu, et al.
Publicado: (2023)
A General Method for Proving Networks Universal Approximation Property
por: Wang, Wei
Publicado: (2025)
por: Wang, Wei
Publicado: (2025)
DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning
por: Guo, Ke, et al.
Publicado: (2025)
por: Guo, Ke, et al.
Publicado: (2025)
Online Adversarial Knowledge Distillation for Graph Neural Networks
por: Wang, Can, et al.
Publicado: (2021)
por: Wang, Can, et al.
Publicado: (2021)
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
por: Yuan, Jinsheng, et al.
Publicado: (2025)
por: Yuan, Jinsheng, et al.
Publicado: (2025)
A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning
por: Wu, Chenlin, et al.
Publicado: (2024)
por: Wu, Chenlin, et al.
Publicado: (2024)
MLDGG: Meta-Learning for Domain Generalization on Graphs
por: Tian, Qin, et al.
Publicado: (2024)
por: Tian, Qin, et al.
Publicado: (2024)
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
por: Han, Xiao, et al.
Publicado: (2021)
por: Han, Xiao, et al.
Publicado: (2021)
Diffusion Guided Adversarial State Perturbations in Reinforcement Learning
por: Sun, Xiaolin, et al.
Publicado: (2025)
por: Sun, Xiaolin, et al.
Publicado: (2025)
Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups
por: Zhu, Yuchen, et al.
Publicado: (2024)
por: Zhu, Yuchen, et al.
Publicado: (2024)
Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
por: Li, Zhuohua, et al.
Publicado: (2025)
por: Li, Zhuohua, et al.
Publicado: (2025)
Fluid Grey 2: How Well Does Generative Adversarial Network Learn Deeper Topology Structure in Architecture That Matches Images?
por: Qiu, Yayan, et al.
Publicado: (2025)
por: Qiu, Yayan, et al.
Publicado: (2025)
How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
por: Rossolini, Giulio
Publicado: (2026)
por: Rossolini, Giulio
Publicado: (2026)
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
por: Chen, Qingfeng, et al.
Publicado: (2025)
por: Chen, Qingfeng, et al.
Publicado: (2025)
Calibrated Language Models and How to Find Them with Label Smoothing
por: Huang, Jerry, et al.
Publicado: (2025)
por: Huang, Jerry, et al.
Publicado: (2025)
Ejemplares similares
-
A Closer Look at Adversarial Suffix Learning for Jailbreaking LLMs: Augmented Adversarial Trigger Learning
por: Wang, Zhe, et al.
Publicado: (2025) -
Domain Adversarial Active Learning for Domain Generalization Classification
por: Chen, Jianting, et al.
Publicado: (2024) -
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks
por: Yang, Guangrui, et al.
Publicado: (2024) -
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
por: Soltany, Milad, et al.
Publicado: (2024) -
Generalized Category Discovery in Federated Graph Learning
por: Yuan, Zhongzheng, et al.
Publicado: (2026)