Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …

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

With the explosive growth of massive data generated by smart Internet of Things (IoT)
devices, federated learning has been envisioned as a promising technique to provide …

Federated learning (FL) is a promising collaborative learning approach in edge computing,
reducing communication costs and addressing the data privacy concerns of traditional cloud …

Federated learning (FL) is a new technology that has been a hot research topic. It enables
the training of an algorithm across multiple decentralized edge devices or servers holding …

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It
enables distributed learning to train on cross-device data, achieving efficient performance …

Federated Learning (FL) is known to perform Machine Learning tasks in a distributed
manner. Over the years, this has become an emerging technology especially with various …

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 …

Federated learning has been a promising distributed machine learning approach in many
fields like e-economic, autodriving and medical imaging for its privacy-aware manner …

In the era of advanced technologies, mobile devices are equipped with computing and
sensing capabilities that gather excessive amounts of data. These amounts of data are …