Federated Averaging (FedAvg) and its variants are prevalent optimization algorithms
adopted in Federated Learning (FL) as they show good model convergence. However, such …

Federated learning (FL) is a new machine learning framework that trains a joint model
across a large number of decentralized computing devices. Existing methods, eg, Federated …

In the big data era, Federated Learning (FL), which allows multiple participants to
collaboratively train a global model without sharing their raw data, emerges as a promising …

Federated learning (FL) is an emerging distributed training paradigm that aims to learn a
common global model without exchanging or transferring the data that are stored locally at …

Federated learning (FL) is a new machine learning framework which trains a joint model
across a large amount of decentralized computing devices. Existing methods, eg, Federated …

Delayed Gradient Averaging (DGA) has gained massive attention for improving the training
efficiency of Federated Learning (FL) at edge networks, by allowing local computation in …

Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple
participants collaboratively to train a global model without uploading raw data. Considering …

Federated Learning (FL) is a type of distributed machine learning, which avoids sharing
privacy and sensitive data with a central server. Despite the advances in FL, current …

Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private raw data …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …