Deep Neural Networks (DNNs) have been a large driver and enabler for AI
...
Motivated by the increasing popularity and importance of large-scale tra...
Efficient distributed training is a principal driver of recent advances ...
We present a partially personalized formulation of Federated Learning (F...
Federated learning (FL) is a distributed machine learning (ML) approach ...
Federated learning has become a popular machine learning paradigm with m...
In this work, we propose new adaptive step size strategies that improve
...
The Granger Causality (GC) test is a famous statistical hypothesis test ...
Federated learning (FL) is an emerging machine learning paradigm involvi...
Byzantine-robustness has been gaining a lot of attention due to the grow...
The practice of applying several local updates before aggregation across...
Federated Learning (FL) has emerged as a promising technique for edge de...
Federated Learning (FL) is an increasingly popular machine learning para...
Federated Learning (FL) has been gaining significant traction across
dif...
Bayesian optimization (BO) is a sample efficient approach to automatical...
It is well understood that client-master communication can be a primary
...
In this work, we consider the optimization formulation of personalized
f...
Modern large-scale machine learning applications require stochastic
opti...
In the last few years, various communication compression techniques have...
Adaptivity is an important yet under-studied property in modern optimiza...
Due to their hunger for big data, modern deep learning models are traine...
The stochastic variance-reduced gradient method (SVRG) and its accelerat...