Recent advances in machine learning have highlighted Federated Learning (FL) as a
promising approach that enables multiple distributed users (so-called clients) to collectively …

Federated learning (FL) is a machine learning technique that allows for on-site data
collection and processing without sacrificing data privacy and transmission. Heterogeneity is …

Federated Learning is a distributed machine learning framework that aims to train a global
shared model while keeping their data locally, and previous researches have empirically …

Conventional federated learning aims at enabling clients to contribute to a global training
process while keeping their own data local. However, as the number of devices on the …

Federated learning (FL) is currently the most widely adopted machine learning model
collaborative training framework under privacy constraints. However, it still faces issues such …

Federated learning is a distributed machine learning approach that keeps data locally while
achieving the utilization of fragmented data and protecting client privacy to a certain extent …

Because of the effectiveness of federated learning in protecting privacy and breaking the"
data silo" phenomenon, it has been widely studied and applied in recent years. Firstly, the …

An optimization algorithm for federated learning, equipped with an incentive mechanism, is
introduced to tackle the challenges of excessive iterations, prolonged training durations, and …

Federated Learning (FL) is a cutting-edge approach to Machine Learning (ML) that allows
for the decentralized training of models, without the need for centralizing the raw data. This …