FLEX: FLEXible Federated Learning Framework

arXiv:2404.06127v1 Announce Type: new
Abstract: In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum …

address applications artificial artificial intelligence arxiv challenges collection continue cs.ai cs.cr data data processing federated federated learning framework handling intelligence paramount privacy privacy and security protection realm security sensitive sensitive data solution