In federated learning (FL), a cluster of local clients are chaired under the coordination of the
global server and cooperatively train one model with privacy protection. Due to the multiple …

Federated learning is a framework for distributed optimization that places emphasis on
communication efficiency. In particular, it follows a client-server broadcast model and is …

We study a model-free federated linear quadratic regulator (LQR) problem where M agents
with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to …

As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model
on a massive amount of devices with infrequent communication. This paper investigates a …

Adaptive optimization has achieved notable success for distributed learning while extending
adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, including (i) …

Big data has played an important role in the development of the economy. However, the
“data isolation” problem largely hinders its full potential. To solve this problem, it is crucial to …

Federated learning (FL) is an emerging distributed machine learning paradigm that aims to
realize model training without gathering the data from data sources to a central processing …

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 …

We study the problem of learning a linear system model from the observations of M clients.
The catch: Each client is observing data from a different dynamical system. This work …

Federated edge learning (FL), as an emerging distributed machine learning paradigm,
allows a mass of edge devices to collaboratively train a global model while preserving …