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Model Poisoning

Papers

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Showing 26–50 of 108 papers

TitleStatusHype
Mitigating Sybils in Federated Learning PoisoningCode0
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with SparsificationCode0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning—0
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling—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
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples—0
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization—0
Turning Federated Learning Systems Into Covert Channels—0
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
FedRAD: Federated Robust Adaptive Distillation—0
FedPerm: Private and Robust Federated Learning by Parameter Permutation—0
Anticipatory Thinking Challenges in Open Worlds: Risk Management—0
Federated Learning-Based Data Collaboration Method for Enhancing Edge Cloud AI System Security Using Large Language Models—0
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy—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
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning—0
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