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 …

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 …

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 …

As an emerging technique, Federated Learning (FL) can jointly train a global model with the
data remaining locally, which effectively solves the problem of data privacy protection …

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 an emerging technology that enables the training of machine
learning models from multiple clients while keeping the data distributed and private. Based …

In Federated Learning (FL), the distributed nature and heterogeneity of client data present
both opportunities and challenges. While collaboration among clients can significantly …

Limited access to large-scale data is a key obstacle to building machine learning (ML)
applications in practice, partly due to a reluctance of information exchange among data …

Federated learning (FL) has found numerous applications in healthcare, finance, and IoT
scenarios. Many existing FL frameworks offer a range of benchmarks to evaluate the …

In this paper, we introduce analytic federated learning (AFL), a new training paradigm that
brings analytical (ie, closed-form) solutions to the federated learning (FL) community. Our …