Many enterprise solutions can greatly benefit from Machine Learning (ML) models that are
created from cross-domain enterprise data. However, many enterprises cannot share data …

Federated Learning represents among the most important techniques used in recent years.
It enables the training of Machine Learning-related models without sharing sensitive data …

Most personalised federated learning (FL) approaches assume that raw data of all clients
are defined in a common subspace ie all clients store their data according to the same …

Federated Learning is an emerging distributed machine learning technique which does not
require the transmission of data to a central server to build a global model. Instead …

Federated learning allows a group of distributed clients to train a common machine learning
model on private data. The exchange of model updates is managed either by a central entity …

Federated Learning (FL) is a machine learning paradigm in which multiple clients participate
to collectively learn a global machine learning model at the central server. It is plausible that …

With the rapid growth in mobile computing, massive amounts of data and computing
resources are now located at the edge. To this end, Federated learning (FL) is becoming a …

Federated learning (FL) is a general distributed machine learning paradigm that provides
solutions for tasks where data cannot be shared directly. Due to the difficulties in …

Federated learning is an emerging collaborative machine-learning paradigm for training
models directly on edge devices. The data remains on the edge device and this method is …

There are situations where data relevant to a machine learning problem are distributed
among multiple locations that cannot share the data due to regulatory, competitiveness, or …