Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple
devices collaboratively learn a machine learning model without sharing their private data …

As a mechanism for devices to update a global model without sharing data, federated
learning bridges the tension between the need for data and respect for privacy. However …

Federated Learning (FL) offers a collaborative training framework, allowing multiple clients
to contribute to a shared model without compromising data privacy. Due to the …

Federated learning (FL) is a promising approach for learning a shared global model on
decentralized data owned by multiple clients without exposing their privacy. In real-world …

Big data has remarkably evolved over the last few years to realize an enormous volume of
data generated from newly emerging services and applications and a massive number of …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

Decentralized federated learning (DFL) is an emerging privacy-preserving machine learning
framework, where multiple data owners cooperate to train a global model without any …

Federated Learning (FL) enables a large number of data owners (aka FL clients) to jointly
train a machine learning model without disclosing private local data. The importance of local …

With the wealth of information produced by social networks, smartphones, medical or
financial applications, speculations have been raised about the sensitivity of such data in …

I heard the terminology “federated learning” for the first time when I was listening to a talk
given by Dr. Catherine Huang from Intel at a workshop of the IEEE Symposium Series on …