Federated Learning (FL) is a distributed machine learning approach in which clients
contribute to learning a global model in a privacy preserved manner. Effective aggregation …

Federated learning is a distributed, privacy-aware learning scenario which trains a single
model on data belonging to several clients. Each client trains a local model on its data and …

The federated learning (FL) paradigm emerges to preserve data privacy during model
training by only exposing clients' model parameters rather than original data. One of the …

Federated Learning (FL) has shown great potential as a privacy-preserving solution to
learning from decentralized data that are only accessible to end devices (ie, clients). The …

Federated Learning (FL) is a collaborative training paradigm whereby a global Machine
Learning (ML) model is trained using typically private and distributed data sources without …

The uneven distribution of local data across different edge devices (clients) results in slow
model training and accuracy reduction in federated learning. Naive federated learning (FL) …

Federated learning (FL) is an important paradigm for training global models from
decentralized data in a privacy-preserving way. Existing FL methods usually assume the …

Federated Learning (FL) has become a practical and popular paradigm in machine learning.
However, currently, there is no systematic solution that covers diverse use cases …

Federated learning (FL) is a promising collaborative learning approach in edge computing,
reducing communication costs and addressing the data privacy concerns of traditional cloud …

Federated machine learning promises to overcome the input privacy challenge in machine
learning. By iteratively updating a model on private clients and aggregating these local …