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Model Poisoning

Papers

Showing 76100 of 108 papers

TitleStatusHype
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing0
MPAF: Model Poisoning Attacks to Federated Learning based on Fake ClientsCode0
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors0
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 Distillation0
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client PerspectiveCode1
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy0
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion0
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks0
On the Security Risks of AutoMLCode0
Byzantine-robust Federated Learning through Collaborative Malicious Gradient FilteringCode1
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples0
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Turning Federated Learning Systems Into Covert Channels0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning0
Robust Federated Learning with Attack-Adaptive AggregationCode1
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation0
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization0
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation0
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments0
BaFFLe: Backdoor detection via Feedback-based Federated Learning0
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