Bandit-based data poisoning attack against federated learning for autonomous driving models
Abstract In Internet of Things (IoT) applications, federated learning is commonly used for
distributedly training models in a privacy-preserving manner. Recently, federated learning is …
distributedly training models in a privacy-preserving manner. Recently, federated learning is …
Data poisoning attacks on federated machine learning
Federated machine learning which enables resource-constrained node devices (eg, Internet
of Things (IoT) devices and smartphones) to establish a knowledge-shared model while …
of Things (IoT) devices and smartphones) to establish a knowledge-shared model while …
SCA: Sybil-based collusion attacks of IIoT data poisoning in federated learning
With the massive amounts of data generated by industrial Internet of Things (IIoT) devices at
all moments, federated learning (FL) enables these distributed distrusted devices to …
all moments, federated learning (FL) enables these distributed distrusted devices to …
PoisonGAN: Generative poisoning attacks against federated learning in edge computing systems
Edge computing is a key-enabling technology that meets continuously increasing
requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the …
requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the …
Desmp: Differential privacy-exploited stealthy model poisoning attacks in federated learning
Federated learning (FL) has become an emerging machine learning technique lately due to
its efficacy in safeguarding the client's confidential information. Nevertheless, despite the …
its efficacy in safeguarding the client's confidential information. Nevertheless, despite the …
A privacy-aware and incremental defense method against GAN-based poisoning attack
F Qiao, Z Li, Y Kong - IEEE Transactions on Computational …, 2023 - ieeexplore.ieee.org
Federated learning is usually utilized as a fraud detection framework in the domain of
financial risk management, which promotes the model accuracy without training data …
financial risk management, which promotes the model accuracy without training data …
Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks
In the age of the Internet of Things (IoT), large numbers of sensors and edge devices are
deployed in various application scenarios; Therefore, collaborative learning is widely used …
deployed in various application scenarios; Therefore, collaborative learning is widely used …
Fedequal: Defending model poisoning attacks in heterogeneous federated learning
With the upcoming edge AI, federated learning (FL) is a privacy-preserving framework to
meet the General Data Protection Regulation (GDPR). Unfortunately, FL is vulnerable to an …
meet the General Data Protection Regulation (GDPR). Unfortunately, FL is vulnerable to an …
Defending poisoning attacks in federated learning via adversarial training method
Recently, federated learning has shown its significant advantages in protecting training data
privacy by maintaining a joint model across multiple clients. However, its model security …
privacy by maintaining a joint model across multiple clients. However, its model security …
RobustFL: Robust federated learning against poisoning attacks in industrial IoT systems
Industrial Internet of Things (IIoT) systems are key enabling infrastructures that sustain the
functioning of production and manufacturing. To satisfy the intelligence demands, federated …
functioning of production and manufacturing. To satisfy the intelligence demands, federated …