Due to the greatly improved capabilities of devices, massive data, and increasing concern about data privacy, Federated Learning (FL) has been increasingly considered for …
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 …
Network intrusion detection methods based on federated learning (FL) and edge computing have great potential for protecting the cybersecurity of the Internet of Things. It overcomes …
The increasing concern for privacy and the use of machine learning on personal data has led researchers to introduce new approaches to machine learning. Federated learning is …
In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks …
Federated Learning (FL) is a machine learning technique that enables multiple parties to collaboratively train a model using their private datasets. Given its decentralized nature, FL …
Software quality is critical, as low quality, or “Code smell,” increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and …
S Alharbi, Y Guo, W Yu - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Internet of Things (IoT) devices generate massive amounts of data from local devices, making federated learning (FL) a viable distributed machine learning paradigm to learn a …
Q Le Roux, E Bourbao, Y Teglia, K Kallas - IEEE Access, 2024 - ieeexplore.ieee.org
Deep learning has significantly transformed face recognition, enabling the deployment of large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep …