Traditional Federated Learning (FL) follows a server-domincated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

The real-world implementation of federated learning is complex and requires research and
development actions at the crossroad between different domains ranging from data science …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Federated Learning (FL) has attracted much attention in recent years because it enables
users with private data sets to train a global model collaboratively without raw data …

In this position paper, we underscore the critical need for a systematic and structured
approach to comparing Federated Learning (FL) frameworks. Given the diversity of FL …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Restrictive rules for data sharing in many industries have led to the development of\ac
{FL}.\ac {FL} is a\ac {ML} technique that allows distributed clients to train models …

The emergency of federated learning (FL) enables distributed data owners to collaboratively
build a global model without sharing their raw data, which creates a new business chance …

Federated Learning (FL) has become a practical and popular paradigm in machine learning.
However, currently, there is no systematic solution that covers diverse use cases …