Abstract The Federated Learning Secure Model Repository presents a novel paradigm to
ensure the trustworthiness of machine learning models generated through federated …

Federated learning (FL) relies on a central authority to oversee and aggregate model
updates contributed by multiple participating parties in the training process. This …

Federated learning (FL) allows untrusted clients to collaboratively train a common machine
learning model, called global model, without sharing their private/proprietary training data …

Federated learning (FL) is an approach within the realm of machine learning (ML) that
allows the use of distributed data without compromising personal privacy. In FL, it becomes …

Federated learning (FL) is a distributed machine learning technique that enables remote
devices to share their local models without sharing their data. While this system benefits …

Federated Learning (FL) offers collaborative model training without data sharing but is
vulnerable to backdoor attacks, where poisoned model weights lead to compromised system …

Federated learning (FL) has gained popularity in the field of machine learning, which allows
multiple participants to collaboratively learn a highly-accurate global model without …

Federated Learning (FL) offers decentralized machine learning (ML) capabilities while
potentially safeguarding data privacy. However, this architecture introduces unique security …

The wide applicability and adoption of federated learning stem from its promise of private
and efficient decentralized training of models. To achieve decentralized training, federated …

Federated Learning (FL) is an emerging machine learning paradigm in which multiple
clients collaboratively train a model without exposing their local datasets. Under this …