Communication efficiency and privacy are two key concerns in modern distributed computing systems. Towards this goal, this article proposes partial sharing private distributed learning (PPDL) algorithms that offer communication ...

Abstract—This paper considers consensus-based distributed Kalman filtering subject to data-falsification attack, where Byzan- tine agents share manipulated data with their neighboring agents. The attack is assumed to be ...

This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) to protect the private information of individual network agents from being acquired by honest-but-curious (HBC) adversaries. The proposed approach ...

We develop a new distributed algorithm to solve the ridge regression problem with feature partitioning of the observation matrix. The proposed algorithm, named D-Ridge, is based on the alternating direction method of ...

This work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output ...

This paper proposes an optimal scheduling policy for a remote estimation problem, where spatio-temporally dependent sensor observations are broadcasted to remote estimators. At each time instant only observations from a ...

This paper proposes an optimal scheduling policy for broadcasting spatio-temporally dependent observations to two remote estimators over a finite time horizon. The system comprises a scheduler that can broadcast one ...

This paper develops a fully distributed differentially-private learning algorithm based on the alternating direction method of multipliers (ADMM) to solve nonsmooth optimization problems. We employ an approximation of the ...

We study privacy-preserving precoder design for decentralized estimation in wireless sensor networks where the sensor nodes want their local information such as the channel state information, observation matrices, and ...

Distributed filtering techniques have emerged as the dominant and most prolific class of filters used in modern monitoring and surveillance applications, such as smart grids. As these techniques rely on information sharing ...