Blockchain-based federated learning for securing internet of things: A comprehensive survey
The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …
significant advantages in agility, responsiveness, and potential environmental benefits. The …
Wild patterns reloaded: A survey of machine learning security against training data poisoning
The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …
and large training datasets. The training data is used to learn new models or update existing …
Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …
preservation demands in artificial intelligence. As machine learning, federated learning is …
Poisoning attacks in federated learning: A survey
G Xia, J Chen, C Yu, J Ma - IEEE Access, 2023 - ieeexplore.ieee.org
Federated learning faces many security and privacy issues. Among them, poisoning attacks
can significantly impact global models, and malicious attackers can prevent global models …
can significantly impact global models, and malicious attackers can prevent global models …
Stdlens: Model hijacking-resilient federated learning for object detection
Federated Learning (FL) has been gaining popularity as a collaborative learning framework
to train deep learning-based object detection models over a distributed population of clients …
to train deep learning-based object detection models over a distributed population of clients …
A robust analysis of adversarial attacks on federated learning environments
Federated Learning is a growing branch of Artificial Intelligence with the wide usage of
mobile computing and IoT technologies. Since this technology uses distributed computing …
mobile computing and IoT technologies. Since this technology uses distributed computing …
Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions
Federated learning (FL) is an approach within the realm of machine learning (ML) that
allows the use of distributed data without compromising personal privacy. In FL, it becomes …
allows the use of distributed data without compromising personal privacy. In FL, it becomes …
On the security & privacy in federated learning
Recent privacy awareness initiatives such as the EU General Data Protection Regulation
subdued Machine Learning (ML) to privacy and security assessments. Federated Learning …
subdued Machine Learning (ML) to privacy and security assessments. Federated Learning …
Differentially private self-normalizing neural networks for adversarial robustness in federated learning
The need for robust, secure and private machine learning is an important goal for realizing
the full potential of the Internet of Things (IoT). Federated Learning has proven to help …
the full potential of the Internet of Things (IoT). Federated Learning has proven to help …
Combating exacerbated heterogeneity for robust models in federated learning
Privacy and security concerns in real-world applications have led to the development of
adversarially robust federated models. However, the straightforward combination between …
adversarially robust federated models. However, the straightforward combination between …