Due to the growth of modern dataset size and the desire to harness computing power of
multiple machines, there is a recent surge of interest in the design of distributed machine …

In this paper, we propose a first-order distributed optimization algorithm that is provably
robust to Byzantine failures–arbitrary and potentially adversarial behavior, where all the …

There is a recent surge of interest in the design of the first-order and the second-order
distributed machine learning algorithms. However, distributed algorithms are sensitive to …

We consider a distributed compressed sensing scenario where many sensors measure
correlated sparse signals and the sensors are connected through a network. Correlation …

We study distributed schemes for high-dimensional sparse linear regression, based on
orthogonal matching pursuit (OMP). Such schemes are particularly suited for settings where …

Network-based CS recovery is used for faster processing of large-scale data, as well as for
sensor networks where the observation vector and sampling matrix are distributed. In this …

In this paper, we develop a greedy algorithm for solving the problem of sparse learning over
a right stochastic network in a distributed manner. The nodes iteratively estimate the sparse …

In this paper, we propose a distributed iterative hard thresholding algorithm, namely,
DiFIGHT, for a network that uses diffusion as the means of intra-network collaboration …

We investigate an existing distributed algorithm for learning sparse signals or data over
networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal …

Compressive wideband spectrum sensing in a cooperative cognitive radio network requires
each secondary user to reconstruct the original signal from the compressed measurement …