:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Du, Mengyao, Zhang, Miao, Pu, Yuwen, Xu, Kai, Ji, Shouling, Yin, Quanjun
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2401.14027
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks
by: Feng, Zhou, et al.
Published: (2025)

Hijack Vertical Federated Learning Models As One Party
by: Qiu, Pengyu, et al.
Published: (2022)

CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models
by: Zeng, Rui, et al.
Published: (2024)

UNIDOOR: A Universal Framework for Action-Level Backdoor Attacks in Deep Reinforcement Learning
by: Ma, Oubo, et al.
Published: (2025)

Poison in the Well: Feature Embedding Disruption in Backdoor Attacks
by: Feng, Zhou, et al.
Published: (2025)

Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration
by: Li, Qinglun, et al.
Published: (2024)

OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
by: Li, Qinglun, et al.
Published: (2024)

Label Forensics: Interpreting Hard Labels in Black-Box Text Classifier
by: Du, Mengyao, et al.
Published: (2025)

SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems
by: Ma, Oubo, et al.
Published: (2024)

A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
by: Yan, Yunlu, et al.
Published: (2023)

Domain-Skewed Federated Learning with Feature Decoupling and Calibration
by: Wang, Huan, et al.
Published: (2026)

BOBA: Byzantine-Robust Federated Learning with Label Skewness
by: Bao, Wenxuan, et al.
Published: (2022)

On the Out-of-Distribution Backdoor Attack for Federated Learning
by: Xu, Jiahao, et al.
Published: (2025)

Federated Unlearning Model Recovery in Data with Skewed Label Distributions
by: Yu, Xinrui, et al.
Published: (2024)

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning
by: Bian, Jieming, et al.
Published: (2025)

Exploiting Label Skewness for Spiking Neural Networks in Federated Learning
by: Yu, Di, et al.
Published: (2024)

Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
by: Sun, Guangyu, et al.
Published: (2022)

HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
by: Qiu, Pengyu, et al.
Published: (2022)

Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization
by: Li, Xiner, et al.
Published: (2023)

Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning
by: Casademunt, Helena, et al.
Published: (2025)

Understanding the Role of Layer Normalization in Label-Skewed Federated Learning
by: Zhang, Guojun, et al.
Published: (2023)

Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning
by: Li, Qingming, et al.
Published: (2024)

Enhancing Reinforcement Learning Fine-Tuning with an Online Refiner
by: Ma, Hao, et al.
Published: (2026)

A Robust Clustered Federated Learning Approach for Non-IID Data with Quantity Skew
by: Ali, Michael Ben, et al.
Published: (2025)

A Unified Generalization Framework for Model Merging: Trade-offs, Non-Linearity, and Scaling Laws
by: Li, Qinglun, et al.
Published: (2026)

Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
by: Wang, Lei, et al.
Published: (2025)

Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning
by: Barkley, Brett, et al.
Published: (2026)

Out-of-Distribution Learning with Human Feedback
by: Bai, Haoyue, et al.
Published: (2024)

Reformulation is All You Need: Addressing Malicious Text Features in DNNs
by: Jiang, Yi, et al.
Published: (2025)

Unveiling the Security Risks of Federated Learning in the Wild: From Research to Practice
by: Chen, Jiahao, et al.
Published: (2026)

Robust Out-of-Distribution Stochastic Optimization
by: Li, Xianyu, et al.
Published: (2026)

Exploring Selective Layer Fine-Tuning in Federated Learning
by: Sun, Yuchang, et al.
Published: (2024)

FTFT: Efficient and Robust Fine-Tuning by Transferring Training Dynamics
by: Du, Yupei, et al.
Published: (2023)

Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning
by: Liu, Yuchen, et al.
Published: (2025)

Learning Memory Kernels in Generalized Langevin Equations
by: Lang, Quanjun, et al.
Published: (2024)

DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
by: Zhou, Zhi, et al.
Published: (2024)

Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
by: Luo, Mengen, et al.
Published: (2024)

Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning
by: Ye, Kai, et al.
Published: (2025)

ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification
by: Wu, Xin, et al.
Published: (2025)

VeFA: Vector-Based Feature Space Adaptation for Robust Model Fine-Tuning
by: Wang, Peng, et al.
Published: (2025)