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Model Poisoning

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Showing 51–100 of 108 papers

TitleStatusHype
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
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection—0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation—0
Security Analysis of SplitFed Learning—0
SLVR: Securely Leveraging Client Validation for Robust Federated Learning—0
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks—0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors—0
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning—0
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks—0
Trojan Horse Hunt in Time Series Forecasting for Space Operations—0
Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks—0
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation—0
VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning—0
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense—0
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion—0
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments—0
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection—0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning—0
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing—0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
Anticipatory Thinking Challenges in Open Worlds: Risk Management—0
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks—0
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples—0
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling—0
BaFFLe: Backdoor detection via Feedback-based Federated Learning—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
Can We Trust the Similarity Measurement in Federated Learning?—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Turning Federated Learning Systems Into Covert Channels—0
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization—0
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach—0
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