Add to ORKGDistributed Mean Estimation (DME) is a central building block in federated learning, where clients send local gradients to a parameter server for averaging and updating the model. Due to communication constraints, clients often use lossy compression techniques to compress the gradients, resulting in estimation inaccuracies. DME is more challenging when clients have diverse network conditions, such as constrained communication budgets and packet losses. In such settings, DME techniques often incur a significant increase in the estimation error leading to degraded learning performance. In this work, we propose a robust DME technique named EDEN that naturally handles heterogeneous communication budgets and packet losses. We derive appealin...
There is a growing interest in the distributed optimization framework that goes under the name of Fe...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
We introduce a framework - Artemis - to tackle the problem of learning in a distributed or federated...
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients s...
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients s...
Distributed Mean Estimation (DME) is a fundamental building block in communication efficient federat...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a...
Decentralized learning algorithms empower interconnected devices to share data and computational res...
In federated learning (FL), a global model is trained at a Parameter Server (PS) by aggregating mode...
Federated Learning (FL) is a distributed machine learning scheme that enables clients to train a sha...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
We consider the problem of estimating the arithmetic average of a finite collection of real vectors ...
In Federated Learning (FL), a number of clients or devices collaborate to train a model without shar...
We consider the problem ofdistributed mean estimation (DME), in which n machines are each given a lo...
There is a growing interest in the distributed optimization framework that goes under the name of Fe...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
We introduce a framework - Artemis - to tackle the problem of learning in a distributed or federated...
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients s...
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients s...
Distributed Mean Estimation (DME) is a fundamental building block in communication efficient federat...
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., m...
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a...
Decentralized learning algorithms empower interconnected devices to share data and computational res...
In federated learning (FL), a global model is trained at a Parameter Server (PS) by aggregating mode...
Federated Learning (FL) is a distributed machine learning scheme that enables clients to train a sha...
Federated Learning is a machine learning paradigm where we aim to train machine learning models in a...
We consider the problem of estimating the arithmetic average of a finite collection of real vectors ...
In Federated Learning (FL), a number of clients or devices collaborate to train a model without shar...
We consider the problem ofdistributed mean estimation (DME), in which n machines are each given a lo...
There is a growing interest in the distributed optimization framework that goes under the name of Fe...
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting po...
We introduce a framework - Artemis - to tackle the problem of learning in a distributed or federated...