Image de couverture de livre

Logits Poisoning Attack in Federated Distillation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Tang, Yuhan, Wu, Zhiyuan, Gao, Bo, Wen, Tian, Wang, Yuwei, Sun, Sheng
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Peak-Controlled Logits Poisoning Attack in Federated Distillation
par: Tang, Yuhan, et autres
Publié: (2024)

Knowledge Distillation in Federated Edge Learning: A Survey
par: Wu, Zhiyuan, et autres
Publié: (2023)

Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
par: Wu, Zhiyuan, et autres
Publié: (2024)

FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation
par: Pan, Quyang, et autres
Publié: (2024)

FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
par: Wu, Zhiyuan, et autres
Publié: (2023)

Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
par: Wu, Zhiyuan, et autres
Publié: (2025)

Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
par: Wu, Zhiyuan, et autres
Publié: (2023)

Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
par: Wu, Zhiyuan, et autres
Publié: (2024)

Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
par: Sun, Shihua, et autres
Publié: (2024)

Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
par: Wang, Yujing, et autres
Publié: (2024)

Logit Distillation on Manifolds: Mapping by Learning
par: Yang, Yiru, et autres
Publié: (2026)

Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head
par: Yang, Penghui, et autres
Publié: (2024)

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
par: Mo, Wenjin, et autres
Publié: (2025)

Sybil-based Virtual Data Poisoning Attacks in Federated Learning
par: Zhu, Changxun, et autres
Publié: (2025)

OmniOPD: Logit-Free On-Policy Distillation via Speculative Verification
par: Zhou, Yuhang, et autres
Publié: (2026)

FedRecAttack: Model Poisoning Attack to Federated Recommendation
par: Rong, Dazhong, et autres
Publié: (2022)

GShield: Mitigating Poisoning Attacks in Federated Learning
par: M., Sameera K., et autres
Publié: (2025)

Poisoning Attacks and Defenses to Federated Unlearning
par: Wang, Wenbin, et autres
Publié: (2025)

LoCa: Logit Calibration for Knowledge Distillation
par: Yang, Runming, et autres
Publié: (2024)

SVAFD: A Secure and Verifiable Co-Aggregation Protocol for Federated Distillation
par: Wen, Tian, et autres
Publié: (2025)

FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
par: Ma, Yuting, et autres
Publié: (2024)

DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences
par: Feng, Chao, et autres
Publié: (2025)

Defending Against Poisoning Attacks in Federated Learning with Blockchain
par: Dong, Nanqing, et autres
Publié: (2023)

Local Environment Poisoning Attacks on Federated Reinforcement Learning
par: Ma, Evelyn, et autres
Publié: (2023)

SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
par: Zhang, Heyi, et autres
Publié: (2025)

Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
par: Li, Bin, et autres
Publié: (2025)

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
par: Xu, Zhangchen, et autres
Publié: (2024)

Provable Watermarking for Data Poisoning Attacks
par: Zhu, Yifan, et autres
Publié: (2025)

FedTrident: Resilient Road Condition Classification Against Poisoning Attacks in Federated Learning
par: Liu, Sheng, et autres
Publié: (2026)

FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks
par: Romandini, Nicolò, et autres
Publié: (2025)

Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures
par: Yu, Yonghao, et autres
Publié: (2024)

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
par: Jiang, Xuefeng, et autres
Publié: (2024)

Robust Federated Learning against Noisy Clients via Masked Optimization
par: Jiang, Xuefeng, et autres
Publié: (2025)

Matching Rates and Optimal Allocation for Federated Probe-Logit Distillation under Heterogeneous Bandwidth Budgets
par: Dubey, Prasanjit, et autres
Publié: (2026)

Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation
par: Liu, Yinuo, et autres
Publié: (2025)

Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
par: Jiang, Yu, et autres
Publié: (2024)

Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
par: Raza, Ali, et autres
Publié: (2022)

AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning
par: Tang, Yuwei, et autres
Publié: (2024)

Exploiting Features and Logits in Heterogeneous Federated Learning
par: Chan, Yun-Hin, et autres
Publié: (2022)

FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
par: Wen, Tian, et autres
Publié: (2026)