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 …

Federated learning enables multiple clients to collaboratively learn machine learning
models in a privacy-preserving manner. However, in real-world scenarios, a key challenge …

In federated learning, clients share a global model that has been trained on decentralized
local client data. Although federated learning shows significant promise as a key approach …

Federated learning (FL) ameliorates privacy concerns in settings where a central server
coordinates learning from data distributed across many clients; rather than sharing the data …

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 (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 …

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 allows multiple clients to collaboratively train a model without
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …

Federated learning is a decentralized learning approach that shows promise for preserving
users' privacy by avoiding local data sharing. However, the heterogeneous data in federated …

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 …