Add to ORKGOne of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full devices participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, this paper develops an inexact alternating direction method of multipliers (ADMM), which is both computation and communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions
The federated learning (FL) framework enables edge clients to collaboratively learn a shared inferen...
The advance of Machine Learning (ML) techniques has become the driving force in the development of A...
To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobi...
One of the crucial issues in federated learning is how to develop efficient optimization algorithms....
Federated learning has shown its advances over the last few years but is facing many challenges, suc...
Federated learning has shown its advances over the last few years but is facing many challenges, suc...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
In the modern paradigm of federated learning, a large number of users are involved in a global learn...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergen...
Federated learning, where algorithms are trained across multiple decentralized devices without shari...
Federated learning (FL) aims to minimize the communication complexity of training a model over heter...
As an emerging technology, federated learning (FL) involves training machine learning models over di...
In the emerging paradigm of Federated Learning (FL), large amount of clients such as mobile devices ...
The federated learning (FL) framework enables edge clients to collaboratively learn a shared inferen...
The advance of Machine Learning (ML) techniques has become the driving force in the development of A...
To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobi...
One of the crucial issues in federated learning is how to develop efficient optimization algorithms....
Federated learning has shown its advances over the last few years but is facing many challenges, suc...
Federated learning has shown its advances over the last few years but is facing many challenges, suc...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
In the modern paradigm of federated learning, a large number of users are involved in a global learn...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergen...
Federated learning, where algorithms are trained across multiple decentralized devices without shari...
Federated learning (FL) aims to minimize the communication complexity of training a model over heter...
As an emerging technology, federated learning (FL) involves training machine learning models over di...
In the emerging paradigm of Federated Learning (FL), large amount of clients such as mobile devices ...
The federated learning (FL) framework enables edge clients to collaboratively learn a shared inferen...
The advance of Machine Learning (ML) techniques has become the driving force in the development of A...
To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobi...