Copertina

FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Xiangyu, Ye, Mang
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Cryptography and Security
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
di: Tan, Zihan, et al.
Pubblicazione: (2024)

Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey
di: Ye, Mang, et al.
Pubblicazione: (2024)

FedPID: An Aggregation Method for Federated Learning
di: Mächler, Leon, et al.
Pubblicazione: (2024)

FedQV: Leveraging Quadratic Voting in Federated Learning
di: Chu, Tianyue, et al.
Pubblicazione: (2024)

FedRecAttack: Model Poisoning Attack to Federated Recommendation
di: Rong, Dazhong, et al.
Pubblicazione: (2022)

FedRE: Robust and Effective Federated Learning with Privacy Preference
di: Xiao, Tianzhe, et al.
Pubblicazione: (2025)

FedGIG: Graph Inversion from Gradient in Federated Learning
di: Xiao, Tianzhe, et al.
Pubblicazione: (2024)

Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions
di: Cai, Zeju, et al.
Pubblicazione: (2024)

FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning
di: Yan, Xinhai, et al.
Pubblicazione: (2025)

FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation
di: Wang, Zhichao, et al.
Pubblicazione: (2024)

FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation
di: Xiao, Chenguang, et al.
Pubblicazione: (2024)

FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
di: Abacha, Fatima Z., et al.
Pubblicazione: (2026)

FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
di: Fereidooni, Hossein, et al.
Pubblicazione: (2023)

FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning
di: Campos, Enrique Mármol, et al.
Pubblicazione: (2024)

FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
di: Zhu, Gongxi, et al.
Pubblicazione: (2024)

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
di: Mulay, Ajinkya Kiran, et al.
Pubblicazione: (2024)

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
di: Jin, Weizhao, et al.
Pubblicazione: (2023)

FedSpaLLM: Federated Pruning of Large Language Models
di: Bai, Guangji, et al.
Pubblicazione: (2024)

FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
di: Jing, Shusen, et al.
Pubblicazione: (2024)

FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning
di: Zeng, Zhihao, et al.
Pubblicazione: (2025)

FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
di: Yang, He, et al.
Pubblicazione: (2026)

LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN
di: Zhang, Yuxing, et al.
Pubblicazione: (2024)

FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
di: Li, Youpeng, et al.
Pubblicazione: (2024)

FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization
di: Zhu, Shunan, et al.
Pubblicazione: (2026)

FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
di: Alsenani, Yousef, et al.
Pubblicazione: (2024)

FedPoP: Federated Learning Meets Proof of Participation
di: İşler, Devriş, et al.
Pubblicazione: (2025)

The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness
di: Hao, Yifan, et al.
Pubblicazione: (2024)

Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
di: Wen, Rui, et al.
Pubblicazione: (2024)

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
di: Shao, Shuo, et al.
Pubblicazione: (2022)

FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
di: Xhemrishi, Marvin, et al.
Pubblicazione: (2023)

FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
di: Yi, Kai, et al.
Pubblicazione: (2024)

FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
di: Han, Sungwon, et al.
Pubblicazione: (2024)

FedNC: A Secure and Efficient Federated Learning Method with Network Coding
di: Shi, Yuchen, et al.
Pubblicazione: (2023)

FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning
di: Zheng, Tianhang, et al.
Pubblicazione: (2024)

Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
di: Chen, Xiaolin, et al.
Pubblicazione: (2021)

Byzantine Failures Harm the Generalization of Robust Distributed Learning Algorithms More Than Data Poisoning
di: Boudou, Thomas, et al.
Pubblicazione: (2025)

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift
di: Thaker, Pratiksha, et al.
Pubblicazione: (2023)

EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection
di: Qian, Yuwen, et al.
Pubblicazione: (2024)

Model Hijacking Attack in Federated Learning
di: Li, Zheng, et al.
Pubblicazione: (2024)

On the Efficiency of Privacy Attacks in Federated Learning
di: Tabassum, Nawrin, et al.
Pubblicazione: (2024)