Federated Learning allows the training of machine learning models by using the
computation and private data resources of many distributed clients. Most existing results on …

Federated Learning allows training machine learning models by using the computation and
private data resources of a large number of distributed clients such as smartphones and IoT …

Federated learning has become a popular method to learn from decentralized
heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models …

Federated Learning (FL) has emerged as a promising distributed learning paradigm that
enables multiple clients to learn a global model collaboratively without sharing their private …

Supervised federated learning (FL) enables multiple clients to share the trained model
without sharing their labeled data. However, potential clients might even be reluctant to label …

Federated learning (FL) enables collaboratively training deep learning models on
decentralized data. However, there are three types of heterogeneities in FL setting bringing …

Federated learning (FL) is an effective machine learning paradigm where multiple clients
can train models based on heterogeneous data in a decentralized manner without …

Federated Learning has become an important learning paradigm due to its privacy and
computational benefits. As the field advances, two key challenges that still remain to be …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Federated learning (FL) is a technique for distributed machine learning that enables the use
of siloed and distributed data. With FL, individual machine learning models are trained …