Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
Fuente:
arXiv
Saved in:
| Main Authors: | Gabrielli, Edoardo, Belli, Dimitri, Matrullo, Zoe, Miori, Vittorio, Tolomei, Gabriele |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Survey on Decentralized Federated Learning
by: Gabrielli, Edoardo, et al.
Published: (2023)
by: Gabrielli, Edoardo, et al.
Published: (2023)
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
by: Raza, Ali, et al.
Published: (2022)
by: Raza, Ali, et al.
Published: (2022)
Turning Federated Learning Systems Into Covert Channels
by: Costa, Gabriele, et al.
Published: (2021)
by: Costa, Gabriele, et al.
Published: (2021)
Model Poisoning Attacks to Federated Learning via Multi-Round Consistency
by: Xie, Yueqi, et al.
Published: (2024)
by: Xie, Yueqi, et al.
Published: (2024)
FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
by: Carney, Jane, et al.
Published: (2025)
by: Carney, Jane, et al.
Published: (2025)
Poison to Detect: Detection of Targeted Overfitting in Federated Learning
by: Mestari, Soumia Zohra El, et al.
Published: (2025)
by: Mestari, Soumia Zohra El, et al.
Published: (2025)
Defending against Data Poisoning Attacks in Federated Learning via User Elimination
by: Galanis, Nick
Published: (2024)
by: Galanis, Nick
Published: (2024)
Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures
by: Wu, Jiahui, et al.
Published: (2025)
by: Wu, Jiahui, et al.
Published: (2025)
GShield: Mitigating Poisoning Attacks in Federated Learning
by: M., Sameera K., et al.
Published: (2025)
by: M., Sameera K., et al.
Published: (2025)
Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures
by: Yu, Yonghao, et al.
Published: (2024)
by: Yu, Yonghao, et al.
Published: (2024)
FedRecAttack: Model Poisoning Attack to Federated Recommendation
by: Rong, Dazhong, et al.
Published: (2022)
by: Rong, Dazhong, et al.
Published: (2022)
A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
by: Yang, Yuxin, et al.
Published: (2024)
by: Yang, Yuxin, et al.
Published: (2024)
FedCC: Robust Federated Learning against Model Poisoning Attacks
by: Jeong, Hyejun, et al.
Published: (2022)
by: Jeong, Hyejun, et al.
Published: (2022)
Local Environment Poisoning Attacks on Federated Reinforcement Learning
by: Ma, Evelyn, et al.
Published: (2023)
by: Ma, Evelyn, et al.
Published: (2023)
Protection against Source Inference Attacks in Federated Learning
by: Athanasiou, Andreas, et al.
Published: (2026)
by: Athanasiou, Andreas, et al.
Published: (2026)
FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization
by: Iftee, Md Akil Raihan, et al.
Published: (2025)
by: Iftee, Md Akil Raihan, et al.
Published: (2025)
Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis
by: Alsulaimawi, Zahir
Published: (2024)
by: Alsulaimawi, Zahir
Published: (2024)
Sybil-based Virtual Data Poisoning Attacks in Federated Learning
by: Zhu, Changxun, et al.
Published: (2025)
by: Zhu, Changxun, et al.
Published: (2025)
Detection of Aerial Spoofing Attacks to LEO Satellite Systems via Deep Learning
by: Wigchert, Jos, et al.
Published: (2024)
by: Wigchert, Jos, et al.
Published: (2024)
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
by: Sun, Shihua, et al.
Published: (2024)
by: Sun, Shihua, et al.
Published: (2024)
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
by: Jiang, Yu, et al.
Published: (2024)
by: Jiang, Yu, et al.
Published: (2024)
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
by: Kumar, K Naveen, et al.
Published: (2024)
by: Kumar, K Naveen, et al.
Published: (2024)
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
by: Xu, Runhua, et al.
Published: (2025)
by: Xu, Runhua, et al.
Published: (2025)
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems
by: M., Sameera K., et al.
Published: (2025)
by: M., Sameera K., et al.
Published: (2025)
PoisonCatcher: Revealing and Identifying LDP Poisoning Attacks in IIoT
by: Shuai, Lisha, et al.
Published: (2024)
by: Shuai, Lisha, et al.
Published: (2024)
Defending Against Neural Network Model Inversion Attacks via Data Poisoning
by: Zhou, Shuai, et al.
Published: (2024)
by: Zhou, Shuai, et al.
Published: (2024)
A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning
by: Purohit, Kiran, et al.
Published: (2023)
by: Purohit, Kiran, et al.
Published: (2023)
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning
by: Wang, Yujing, et al.
Published: (2024)
by: Wang, Yujing, et al.
Published: (2024)
Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution
by: Rattanavipanon, Norrathep, et al.
Published: (2024)
by: Rattanavipanon, Norrathep, et al.
Published: (2024)
Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks
by: Yuan, Danni, et al.
Published: (2023)
by: Yuan, Danni, et al.
Published: (2023)
PACE: Poisoning Attacks on Learned Cardinality Estimation
by: Zhang, Jintao, et al.
Published: (2024)
by: Zhang, Jintao, et al.
Published: (2024)
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
by: Wang, Jinbo, et al.
Published: (2024)
by: Wang, Jinbo, et al.
Published: (2024)
Sharpness-Aware Data Poisoning Attack
by: He, Pengfei, et al.
Published: (2023)
by: He, Pengfei, et al.
Published: (2023)
Detecting and Preventing Data Poisoning Attacks on AI Models
by: Kure, Halima I., et al.
Published: (2025)
by: Kure, Halima I., et al.
Published: (2025)
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)
by: Xu, Zhangchen, et al.
Published: (2024)
VMGuard: Reputation-Based Incentive Mechanism for Poisoning Attack Detection in Vehicular Metaverse
by: Lotfi, Ismail, et al.
Published: (2024)
by: Lotfi, Ismail, et al.
Published: (2024)
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
by: Xie, Chulin, et al.
Published: (2022)
by: Xie, Chulin, et al.
Published: (2022)
Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
by: Dissanayake, Nisith, et al.
Published: (2025)
by: Dissanayake, Nisith, et al.
Published: (2025)
Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
by: Jia, Yuqi, et al.
Published: (2024)
by: Jia, Yuqi, et al.
Published: (2024)
SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
by: Zhang, Heyi, et al.
Published: (2025)
by: Zhang, Heyi, et al.
Published: (2025)
Similar Items
-
A Survey on Decentralized Federated Learning
by: Gabrielli, Edoardo, et al.
Published: (2023) -
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
by: Raza, Ali, et al.
Published: (2022) -
Turning Federated Learning Systems Into Covert Channels
by: Costa, Gabriele, et al.
Published: (2021) -
Model Poisoning Attacks to Federated Learning via Multi-Round Consistency
by: Xie, Yueqi, et al.
Published: (2024) -
FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
by: Carney, Jane, et al.
Published: (2025)