Federated learning is a recently proposed distributed machine learning paradigm for privacy
preservation, which has found a wide range of applications where data privacy is of primary …

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Federated learning and analytics are a distributed approach for collaboratively learning
models (or statistics) from decentralized data, motivated by and designed for privacy …

We introduce ProxSkip—a surprisingly simple and provably efficient method for minimizing
the sum of a smooth ($ f $) and an expensive nonsmooth proximable ($\psi $) function. The …

Federated learning (FL) is a well established method for performing machine learning tasks
over massively distributed data. However in settings where data is distributed in a non-iid …

We provide a new analysis of local SGD, removing unnecessary assumptions and
elaborating on the difference between two data regimes: identical and heterogeneous. In …

The performance of a model in machine learning problems highly depends on the dataset
and training algorithms. Choosing the right training algorithm can change the tale of a …

Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed
training. The scheme can reach a linear speedup with respect to the number of workers, but …

We study the asynchronous stochastic gradient descent algorithm, for distributed training
over $ n $ workers that might be heterogeneous. In this algorithm, workers compute …