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Model Poisoning

Papers

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Showing 26–50 of 108 papers

TitleStatusHype
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems—0
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense—0
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning—0
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated LearningCode0
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning—0
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with AutoencoderCode0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Leverage Variational Graph Representation For Model Poisoning on Federated LearningCode0
Poisoning Decentralized Collaborative Recommender System and Its Countermeasures—0
Robust Federated Contrastive Recommender System against Model Poisoning Attack—0
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing—0
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach—0
Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)—0
Can We Trust the Similarity Measurement in Federated Learning?—0
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification—0
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks—0
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection—0
FedDefender: Client-Side Attack-Tolerant Federated LearningCode1
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
Anticipatory Thinking Challenges in Open Worlds: Risk Management—0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers—0
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