Federated Learning for Image Classification is a paradigm where machine learning models are trained across multiple devices, such as smartphones or edge servers, without exchanging data. This approach ...

Federated Learning for Text Generation is an innovative approach to training machine learning models that involve multiple participants, each holding their own data. This decentralized method ensures ...

Abstract: 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 distributed and ...

Federated Learning is a decentralised and privacy-friendly form of machine learning. This means that there is no need for a central database to hold all of the sensitive data, so these data cannot be ...

One of the key challenges of machine learning is the need for large amounts of data. Gathering training datasets for machine learning models poses privacy, security, and processing risks that ...

Federated Learning (FL) stands at the intersection of privacy preservation and decentralized data use, revolutionizing practical machine learning. This approach maintains data on local devices, ...