Due to the limited resources and data privacy issue, last decade witnesses the fast
development of Distributed Machine Learning (DML) at network edges. Among all the …

Federated Learning (FL) is a distributed machine learning technology that trains models on
large-scale distributed devices while keeping training data localized and privatized …

Federated learning (FL) is an emerging machine learning paradigm that allows to
accomplish model training without aggregating data at a central server. Most studies on FL …

Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of
the data across different clients which gives rise to the client drift phenomenon. In fact …

This work presents an efficient data-centric client selection approach, named DICE, to
enable federated learning (FL) over distributed edge networks. Prior research focused on …

Federated Learning has sparked increasing interest as a promising approach to utilize large
amounts of data stored on network edge devices. Federated Averaging is the most widely …

We consider a standard federated learning (FL) setup where a group of clients periodically
coordinate with a central server to train a statistical model. We develop a general algorithmic …

Data generated at the network edge can be processed locally by leveraging the emerging
technology of Federated Learning (FL). However, non-IID local data will lead to degradation …

Federated learning (FL) is a fast-developing technique that allows multiple workers to train a
global model based on a distributed dataset. Conventional FL (FedAvg) employs gradient …

Federated Learning (FL) is a recent development in distributed machine learning that
collaboratively trains models without training data leaving client devices, preserving data …