Many existing FL methods assume clients with fully-labeled data, while i...
When the data used for reinforcement learning (RL) are collected by mult...
Minimax optimization has seen a surge in interest with the advent of mod...
Federated Averaging (FedAvg) remains the most popular algorithm for Fede...
Data-heterogeneous federated learning (FL) systems suffer from two
signi...
Federated Learning (FL) is a variant of distributed learning where edge
...
Most data generated by modern applications is stored in the cloud, and t...
Since reinforcement learning algorithms are notoriously data-intensive, ...
Federated Learning (FL) under distributed concept drift is a largely
une...
Federated learning (FL) facilitates collaboration between a group of cli...
Federated learning (FL) enables edge-devices to collaboratively learn a ...
In this paper, we consider nonconvex minimax optimization, which is gain...
In classical federated learning, the clients contribute to the overall
t...
We study the problem of estimating at a central server the mean of a set...
Personalized federated learning (FL) aims to train model(s) that can per...
In this paper we consider the problem of best-arm identification in
mult...
In multi-server queueing systems where there is no central queue holding...
The federated learning (FL) framework trains a machine learning model us...
Communication of model updates between client nodes and the central
aggr...
The maximum possible throughput (or the rate of job completion) of a
mul...
Due to communication constraints and intermittent client availability in...
Federated learning is a distributed optimization paradigm that enables a...
Erasure coding has been recently employed as a powerful method to mitiga...
In federated optimization, heterogeneity in the clients' local datasets ...
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous
...
Due to the massive size of the neural network models and training datase...
Distributed stochastic gradient descent (SGD) is essential for scaling t...
Federated learning (FL) is a machine learning setting where many clients...
We consider a multi-armed bandit framework where the rewards obtained by...
Gaussian Processes (GPs) with an appropriate kernel are known to provide...
This paper introduces Selective-Backprop, a technique that accelerates t...
The trade-off between convergence error and communication delays in
dece...
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
We consider storage systems in which K files are stored over N nodes. A
...
Large-scale machine learning training, in particular distributed stochas...
We consider a correlated multi-armed bandit problem in which rewards of ...
State-of-the-art distributed machine learning suffers from significant d...
We consider a novel multi-armed bandit framework where the rewards obtai...
This paper studies the problem of learning the probability distribution...
Large-scale machine learning and data mining applications require comput...
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous
...