Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

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 …

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 …

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 …