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Model Poisoning

Papers

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Showing 76–100 of 108 papers

TitleStatusHype
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers—0
Mixed Strategy Game Model Against Data Poisoning Attacks—0
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients—0
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems—0
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning—0
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning—0
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks—0
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning—0
Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach—0
Performance Weighting for Robust Federated Learning Against Corrupted Sources—0
PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning—0
pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology—0
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion—0
Poisoning Bayesian Inference via Data Deletion and Replication—0
Poisoning Decentralized Collaborative Recommender System and Its Countermeasures—0
Poster: Sponge ML Model Attacks of Mobile Apps—0
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy—0
RepuNet: A Reputation System for Mitigating Malicious Clients in DFL—0
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks—0
Robust Federated Contrastive Recommender System against Model Poisoning Attack—0
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated LearningCode0
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning AttacksCode0
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated LearningCode0
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of RobustnessCode0
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated LearningCode0
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