Federated learning (FL) enables collaborative model training across distributed clients (eg,
edge devices) without sharing raw data. Yet, FL can be computationally expensive as the …

In SplitFed learning (SFL), a global model is split into two segments, where distributed
clients train the first segment in a federated manner and a main server trains the other …

Split federated learning (SFL) is a recent distributed approach for collaborative model
training among multiple clients. In SFL, a global model is typically split into two parts, where …

Federated learning (FL) is a distributed machine learning scheme that enables clients to
train a shared global model without exchanging local data. In FL, the presence of label noise …

Federated learning is a popular distributed learning approach for training a machine
learning model without disclosing raw data. It consists of a parameter server and a possibly …

In federated learning (FL), distributed users collaboratively train a neural network model
under the coordination of a central server. However, during the training process, clients often …

Federated Learning (FL) is a decentralized machine learning framework that enables
collaborative model training while respecting data privacy. In various applications, non …

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving
collaborative learning, enabling multiple devices to jointly train a global model without …