Federated learning (FL) has become the next generation of machine learning (ML) by
avoiding local data sharing with a central server. While this becomes a major advantage to …

Federated learning enables cooperative computation between multiple participants while
protecting user privacy. Currently, federated learning algorithms assume that all participants …

Federated learning (FL) is a promising paradigm to realize distributed machine learning on
heterogeneous clients without exposing their private data. However, there is the risk of …

Federated learning enables multiple users to collaboratively train a global model using the
users' private data on users' local machines. This way, users are not required to share their …

Federated learning (FL) is a privacy-preserving approach in Artificial Intelligence (AI) that
involves exchanging intermediate training parameters instead of raw data, thereby avoiding …

Federated learning has been increasingly studied to cope with the scalability and privacy
issues characterizing current and upcoming large-scale infrastructures, such as the Internet …

Federated learning (FL) enables collaborative training of machine learning (ML) models
while preserving user data privacy. Existing FL approaches can potentially facilitate …

Recent advances in Blockchain-based Federated Learning (FL) aim to address the inherent
limitations of traditional FL, such as single node failure and the lack of an appropriate …

This work analyzes the integration of a permissioned blockchain network for a verification
mechanism to perform decentralized, robust, and privacy-friendly federated learning (FL). In …

Federated learning (FL), as a distributed machine learning approach, has drawn a great
amount of attention in recent years. FL shows an inherent advantage in privacy preservation …