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 …
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 …
FDSFL: Filtering Defense Strategies toward Targeted Poisoning Attacks in IIoT-Based Federated Learning Networking System
As a novel distributed machine learning scheme, federated learning (FL) efficiently realizes
the collaborative training of models by global participants while also protecting their data …
the collaborative training of models by global participants while also protecting their data …
SBPA: sybil-based backdoor poisoning attacks for distributed big data in AIoT-based federated learning system
Federated learning (FL) enables a great deal of distributed independent participants to
collaborate in training without sharing data. Malicious adversary can poison the local model …
collaborate in training without sharing data. Malicious adversary can poison the local model …
Clean‐label poisoning attacks on federated learning for IoT
Federated Learning (FL) is suitable for the application scenarios of distributed edge
collaboration of the Internet of Things (IoT). It can provide data security and privacy, which is …
collaboration of the Internet of Things (IoT). It can provide data security and privacy, which 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 …
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 …
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 …
Multitentacle federated learning over software-defined industrial internet of things against adaptive poisoning attacks
G Li, J Wu, S Li, W Yang, C Li - IEEE Transactions on Industrial …, 2022 - ieeexplore.ieee.org
Software-defined industrial Internet of things (SD-IIoT) exploits federated learning to process
the sensitive data at edges, while adaptive poisoning attacks threat the security of SD-IIoT …
the sensitive data at edges, while adaptive poisoning attacks threat the security of SD-IIoT …
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 …