Principal component analysis (PCA) is a fundamental primitive of many data analysis, array
processing, and machine learning methods. In applications where extremely large arrays of …

To leverage the enormous amount of unlabeled data on distributed edge devices, we
formulate a new problem in federated learning called federated unsupervised …

In this letter, we study distributed optimization, where a network of agents, abstracted as a
directed graph, collaborates to minimize the average of locally known convex functions …

We study distributed optimization to minimize a sum of smooth and strongly-convex
functions. Recent work on this problem uses gradient tracking to achieve linear convergence …

Distributed machine learning algorithms enable learning of models from datasets that are
distributed over a network without gathering the data at a centralized location. While efficient …

In this paper, we study distributed stochastic optimization to minimize a sum of smooth and
strongly-convex local cost functions over a network of agents, communicating over a strongly …

In this article, we provide a distributed optimization algorithm, termed as TV-AB, that
minimizes a sum of convex functions over time-varying, random directed graphs. Contrary to …

Factorizing a low-rank matrix into two matrix factors with low dimensions from its noisy
observations is a classical but challenging problem arising from real-world applications. This …

In this paper, we discuss distributed optimization over directed graphs, where doubly
stochastic weights cannot be constructed. Most of the existing algorithms overcome this …

We consider a distributed non-convex optimization where a network of agents aims at
minimizing a global function over the Stiefel manifold. The global function is represented as …