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Model Poisoning

Papers

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Showing 1–50 of 108 papers

TitleStatusHype
RepuNet: A Reputation System for Mitigating Malicious Clients in DFL—0
Federated Learning-Based Data Collaboration Method for Enhancing Edge Cloud AI System Security Using Large Language Models—0
Trojan Horse Hunt in Time Series Forecasting for Space Operations—0
Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach—0
GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework—0
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning—0
Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks—0
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning—0
Poisoning Bayesian Inference via Data Deletion and Replication—0
SLVR: Securely Leveraging Client Validation for Robust Federated Learning—0
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated LearningCode0
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences—0
SoK: Benchmarking Poisoning Attacks and Defenses in Federated LearningCode2
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning—0
VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning—0
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning—0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution—0
FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated LearningCode0
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks—0
PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning—0
pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology—0
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated LearningCode0
HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning—0
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks—0
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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