Image de couverture de livre

Beyond performance-wise Contribution Evaluation in Federated Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Pejo, Balazs
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
par: Xhemrishi, Marvin, et autres
Publié: (2025)

Private and Robust Contribution Evaluation in Federated Learning
par: Velez, Delio Jaramillo, et autres
Publié: (2026)

On the Fragility of Contribution Score Computation in Federated Learning
par: Pejo, Balazs, et autres
Publié: (2025)

FRIDA: Free-Rider Detection using Privacy Attacks
par: Recasens, Pol G., et autres
Publié: (2024)

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

ConDa: Fast Federated Unlearning with Contribution Dampening
par: Chundawat, Vikram S, et autres
Publié: (2024)

History-Aware and Dynamic Client Contribution in Federated Learning
par: Ghosh, Bishwamittra, et autres
Publié: (2024)

Evaluating Adversarial Attacks on Federated Learning for Temperature Forecasting
par: Chichifoi, Karina, et autres
Publié: (2025)

On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
par: Wang, Zijian, et autres
Publié: (2025)

Attacks on fairness in Federated Learning
par: Rance, Joseph, et autres
Publié: (2023)

Federated Learning with Relative Fairness
par: Nakakita, Shogo, et autres
Publié: (2024)

Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
par: Wang, Houzhe, et autres
Publié: (2026)

Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge
par: Bertoli, Gustavo de Carvalho
Publié: (2026)

On the Efficiency of Privacy Attacks in Federated Learning
par: Tabassum, Nawrin, et autres
Publié: (2024)

Model Hijacking Attack in Federated Learning
par: Li, Zheng, et autres
Publié: (2024)

Preserving Privacy and Security in Federated Learning
par: Nguyen, Truc, et autres
Publié: (2022)

Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics
par: Romandini, Nicolò, et autres
Publié: (2024)

On the Out-of-Distribution Backdoor Attack for Federated Learning
par: Xu, Jiahao, et autres
Publié: (2025)

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

Temporal Analysis of Adversarial Attacks in Federated Learning
par: Mapakshi, Rohit, et autres
Publié: (2025)

FedPID: An Aggregation Method for Federated Learning
par: Mächler, Leon, et autres
Publié: (2024)

Turning Federated Learning Systems Into Covert Channels
par: Costa, Gabriele, et autres
Publié: (2021)

Traceable Black-box Watermarks for Federated Learning
par: Xu, Jiahao, et autres
Publié: (2025)

Fairness-Constrained Optimization Attack in Federated Learning
par: Kasyap, Harsh, et autres
Publié: (2025)

Survey of Privacy Threats and Countermeasures in Federated Learning
par: Hayashitani, Masahiro, et autres
Publié: (2024)

Competitive Advantage Attacks to Decentralized Federated Learning
par: Jia, Yuqi, et autres
Publié: (2023)

Backdoor Attacks in Peer-to-Peer Federated Learning
par: Syros, Georgios, et autres
Publié: (2023)

Differentially Private Block-wise Gradient Shuffle for Deep Learning
par: Zagardo, David
Publié: (2024)

Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach
par: Nasir, Hira, et autres
Publié: (2026)

Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
par: Chang, Wenhan, et autres
Publié: (2024)

Beyond Static Tools: Evaluating Large Language Models for Cryptographic Misuse Detection
par: Masood, Zohaib, et autres
Publié: (2024)

Watch Out for the Lifespan: Evaluating Backdoor Attacks Against Federated Model Adaptation
par: Vuillod, Bastien, et autres
Publié: (2025)

Convergent Differential Privacy Analysis for General Federated Learning
par: Sun, Yan, et autres
Publié: (2024)

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

Federated Learning Clients Clustering with Adaptation to Data Drifts
par: Li, Minghao, et autres
Publié: (2024)

One-shot Empirical Privacy Estimation for Federated Learning
par: Andrew, Galen, et autres
Publié: (2023)

FedQV: Leveraging Quadratic Voting in Federated Learning
par: Chu, Tianyue, et autres
Publié: (2024)

Differentially Private Federated Learning: A Systematic Review
par: Fu, Jie, et autres
Publié: (2024)

Mitigating Membership Inference Vulnerability in Personalized Federated Learning
par: Jung, Kangsoo, et autres
Publié: (2025)

FL-Defender: Combating Targeted Attacks in Federated Learning
par: Jebreel, Najeeb, et autres
Publié: (2022)