Abstract Recently, Federated Learning (FL) gained considerable popularity as it offers an
isolated and privacy-preserving mechanism to train Machine Learning models on unseen …

In the era of deep learning, federated learning (FL) presents a promising approach that
allows multi-institutional data owners, or clients, to collaboratively train machine learning …

Federated learning (FL) is a promising decentralized deep learning technology, which
allows users to update models cooperatively without sharing their data. FL is reshaping …

Federated learning (FL) offers a promising approach to efficient machine learning with
privacy protection in distributed environments, such as Internet of Things (IoT) and mobile …

Data is readily available with the growing number of smart and IoT devices. However,
application-specific data is available in small chunks and distributed across demographics …

Data is readily available with the growing number of smart and IoT devices. Industries of
different sectors follow technological advancement to be benefited from data sharing …

Privacy and data security have become the new hot topic for regulators in recent years. As a
result, Federated Learning (FL)(also called collaborative learning) has emerged as a new …

Federated Learning (FL) is a multiparty learning computing approach that can aid privacy-
preservation machine learning. However, FL has several potential security and privacy …

As an emerging learning paradigm, Federated Learning (FL) enables data owners to
collaborate training a model while keeps data locally. However, classic FL methods are …

Federated machine learning (FL) allows to collectively train models on sensitive data as only
the clients' models and not their training data need to be shared. However, despite the …