Under horizontal federated learning (HFL) in the Internet of Things (IoT) scenarios, different
user data sets have significant similarities on the feature spaces, the final goal is to build a …

Federated Learning (FL) aims to train a globally shared model by employing local data
samples generated by data sources. The inherent heterogeneity of IoT environments, in …

Federated learning (FL) enables multiple clients to train models collaboratively without
sharing local data, which has achieved promising results in different areas, including the …

Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML)
models based on distributed data sets. It enables In-Edge AI, preserves data locality …

Federated learning (FL) has been recognized as a promising collaborative on-device
machine learning method in the design of Internet of Things (IoT) systems. However, most …

Machine learning (ML), and deep learning (DL) in particular, play a vital role in providing
smart services to the industry. These techniques, however, suffer from privacy and security …

As the Internet-of-Things devices are being very widely adopted in all fields, such as smart
houses, healthcare, and transportation, extremely huge amounts of data are being gathered …

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many
intelligent IoT services and applications are emerging. Recently, federated learning is …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Abstract Recently, Federated Learning (FL) has been explored as a new paradigm that
preserves both data privacy and end-users knowledge while reducing latency during model …