Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Mobile and wearable devices have enabled numerous applications, including activity
tracking, wellness monitoring, and human–computer interaction, that measure and improve …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to
abide by the existing privacy regulations to preserve patients' anonymity. However, data is …

Federated Learning (FL) has recently received significant interests thanks to its capability of
protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for …

Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …

Recent technological developments, such as the Internet of Things (IoT), artificial
intelligence, edge, and cloud computing, have paved the way in transforming traditional …