Nowadays, many edge computing service providers expect to leverage the computational
power and data of edge nodes to improve their models without transmitting data. Federated …

Federated learning (FL) trains a model over multiple datasets by collecting the local models
rather than raw data, which can help facilitate distributed data analysis in many real-world …

Federated learning facilitates the collaborative training of a global model among distributed
clients without sharing their training data. Secure aggregation, a new security primitive for …

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

Federated learning has emerged as a promising paradigm by collaboratively training a
global model through sharing local gradients without exposing raw data. However, the …

Federated learning (FL) has demonstrated tremendous success in various mission-critical
large-scale scenarios. However, such promising distributed learning paradigm is still …

Federated learning enables clients to train a machine learning model jointly without sharing
their local data. However, due to the centrality of federated learning framework and the …

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 has recently emerged as a paradigm promising the benefits of
harnessing rich data from diverse sources to train high quality models, with the salient …

Secure federated learning is a privacy-preserving framework to improve machine learning
models by training over large volumes of data collected by mobile users. This is achieved …