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Found 12 papers, 1 papers with code
Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm
Extensive experiments on real-world datasets verify the effectiveness and robustness of LFBA against image processing operations and the state-of-the-art backdoor defenses, as well as its inherent stealthiness in both spatial and frequency space, making it resilient against frequency inspection.
FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning
In this work, we propose a new stealthy and robust backdoor attack with flexible triggers against FL defenses.
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering
It is therefore currently impossible to ensure Byzantine robustness and confidentiality of updates without assuming a semi-honest majority.
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks
In this ML technique, only parameters and certain metadata would be communicated.
MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution
Federated learning is an emerging concept in the domain of distributed machine learning.
FLVoogd: Robust And Privacy Preserving Federated Learning
In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy.
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
To further explore the properties of two backdoor attacks in Federated GNNs, we evaluate the attack performance for a different number of clients, trigger sizes, poisoning intensities, and trigger densities.
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints
Backdoor attack is a type of serious security threat to deep learning models. An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor.
HoneyCar: A Framework to Configure HoneypotVulnerabilities on the Internet of Vehicles
By taking a game-theoretic approach, we model the adversarial interaction as a repeated imperfect-information zero-sum game in which the IoV network administrator chooses a set of vulnerabilities to offer in a honeypot and a strategic attacker chooses a vulnerability of the IoV to exploit under uncertainty.
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without revealing private local information.
PIVODL: Privacy-preserving vertical federated learning over distributed labels
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data.
