As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …

We consider decentralized stochastic optimization with the objective function (eg data
samples for machine learning tasks) being distributed over n machines that can only …

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

We propose a general yet simple theorem describing the convergence of SGD under the
arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of …

Recent developments on large-scale distributed machine learning applications, eg, deep
neural networks, benefit enormously from the advances in distributed non-convex …

We consider decentralized machine learning over a network where the training data is
distributed across $ n $ agents, each of which can compute stochastic model updates on …