In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

Federated learning (FL) has received significant attention from both academia and industry,
as an emerging paradigm for building machine learning models in a communication-efficient …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …

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 …

In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Federated learning (FL) allows a server to learn a machine learning (ML) model across
multiple decentralized clients that privately store their own training data. In contrast with …

Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple
devices collaboratively learn a machine learning model without sharing their private data …

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 (FL) is an important paradigm for training global models from
decentralized data in a privacy-preserving way. Existing FL methods usually assume the …