Federated learning (FL) is promising in supporting collaborative learning applications that
involve large datasets, massively distributed data owners and unreliable network …

Federated Learning (FL) has emerged as a privacy-preserving distributed Machine Learning
paradigm, which collaboratively trains a shared global model across a number of end …

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) is an emerging distributed machine learning paradigm which
addresses critical data privacy issues in machine learning by enabling clients, using an …

Federated learning has been widely studied and applied to various scenarios, such as
financial credit, medical identification, and so on. Under these settings, federated learning …

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 …

Federated learning (FL) is a distributed machine learning (ML) technique that enables
collaborative training in which devices perform learning using a local dataset while …

As a novel distributed learning mechanism, federated learning has drawn widespread
attention by allowing multiple parties to train an accurate model collaboratively without …

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 …

Federated learning (FL) enables collaborative training of machine learning (ML) models
while preserving user data privacy. Existing FL approaches can potentially facilitate …