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Model Poisoning

Papers

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Showing 51–100 of 108 papers

TitleStatusHype
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
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
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures—0
A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning—0
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning—0
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection—0
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning AttacksCode0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
Poster: Sponge ML Model Attacks of Mobile Apps—0
WW-FL: Secure and Private Large-Scale Federated Learning—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?—0
FedCC: Robust Federated Learning against Model Poisoning Attacks—0
Security Analysis of SplitFed Learning—0
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks—0
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks—0
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks—0
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated LearningCode0
FedPerm: Private and Robust Federated Learning by Parameter Permutation—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Performance Weighting for Robust Federated Learning Against Corrupted Sources—0
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling—0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy—0
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error AnalysisCode0
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing—0
MPAF: Model Poisoning Attacks to Federated Learning based on Fake ClientsCode0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors—0
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of RobustnessCode0
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with SparsificationCode0
FedRAD: Federated Robust Adaptive Distillation—0
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy—0
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion—0
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks—0
On the Security Risks of AutoMLCode0
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples—0
Turning Federated Learning Systems Into Covert Channels—0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning—0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation—0
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization—0
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation—0
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