Add to ORKGGraduate School of Artificial Intelligence ArtificiFederated learning is a privacy-preserving machine learning problem in a distributed setting. Due to the challenges of system heterogeneity across clients, all clients cannot participate in every communication. Furthermore, statistical heterogeneity can lead to the imbalance of training information across clients and makes the convergence slower in federated learning, as a well-known problem, client drift. To solve this problem, most studies have proposed to switch the model aggregation algorithm to balance it. On the other hand, some works have attempted to select informative clients to alleviate the problem. Although this approach showed performance improvement in earlier communication ro...
We propose a communication efficient approach for federated learn- ing in heterogeneous environments...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated learning is an approach to distributed machine learning where a global model is learned by...
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) ...
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine le...
The issue of potential privacy leakage during centralized AI's model training has drawn intensive co...
International audienceThis work addresses the problem of optimizing communications between server an...
Federated learning (FL) enables collaborative learning between parties, called clients, without shar...
Publisher Copyright: AuthorFederated learning (FL) is a novel machine learning setting that enables ...
International audienceWhile client sampling is a central operation of current state-of-the-art feder...
Federated Learning (FL) has shown great potential as a privacy-preserving solution to training a cen...
The paradigm of Federated learning (FL) deals with multiple clients participating in collaborative t...
The advent of machine learning techniques has given rise to modern devices with built-in models for ...
The advent of machine learning techniques has given rise to modern devices with built-in models for ...
The issue of potential privacy leakage during centralized AI's model training has drawn intensive co...
We propose a communication efficient approach for federated learn- ing in heterogeneous environments...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated learning is an approach to distributed machine learning where a global model is learned by...
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) ...
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine le...
The issue of potential privacy leakage during centralized AI's model training has drawn intensive co...
International audienceThis work addresses the problem of optimizing communications between server an...
Federated learning (FL) enables collaborative learning between parties, called clients, without shar...
Publisher Copyright: AuthorFederated learning (FL) is a novel machine learning setting that enables ...
International audienceWhile client sampling is a central operation of current state-of-the-art feder...
Federated Learning (FL) has shown great potential as a privacy-preserving solution to training a cen...
The paradigm of Federated learning (FL) deals with multiple clients participating in collaborative t...
The advent of machine learning techniques has given rise to modern devices with built-in models for ...
The advent of machine learning techniques has given rise to modern devices with built-in models for ...
The issue of potential privacy leakage during centralized AI's model training has drawn intensive co...
We propose a communication efficient approach for federated learn- ing in heterogeneous environments...
Federated learning allows the training of a model from the distributed data of many clients under th...
Federated learning is an approach to distributed machine learning where a global model is learned by...