• Consensus-based Distributed Total Least-squares Estimation Using Parametric Semidefinite Programming 

      Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Arablouei, Reza; Werner, Stefan (Chapter, 2019)
      We propose a new distributed algorithm to solve the total least-squares (TLS) problem when data are distributed over a multi-agent network. To develop the proposed algorithm, named distributed ADMM TLS (DA-TLS), we reformulate ...
    • Coordinated Data-Falsification Attacks in Consensus-based Distributed Kalman Filtering 

      Moradi, Ashkan; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Stefan (Chapter; Peer reviewed, 2019)
      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 ...
    • Cost-Aware Dual Prediction Scheme for Reducing Transmissions at IoT Sensor Nodes 

      Håkansson, Victor Wattin; Dasanadoddi Venkategowda, Naveen Kumar; Kraemer, Frank Alexander; Werner, Stefan (Chapter, 2019)
      This paper develops a method for deciding when to update the prediction model or transmit a set of measurements from the sensor to the fusion centre (FC) to achieve minimal data transmission in a dual prediction scheme ...
    • Distributed Learning with Non-Smooth Objective Functions 

      Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Arablouei, Reza; Werner, Stefan (Chapter, 2020)
      We develop a new distributed algorithm to solve a learning problem with non-smooth objective functions when data are distributed over a multi-agent network. We employ a zeroth-order method to minimize the associated augmented ...
    • Distributed Ridge Regression with Feature Partitioning 

      Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Arablouei, Reza; Werner, Stefan (Chapter, 2019)
      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 ...
    • Linear MMSE Precoder Combiner Designs for Decentralized Estimation in Wireless Sensor Networks 

      Rajput, Kunwar; Verma, Yogesh; Dasanadoddi Venkategowda, Naveen Kumar; Jagannatham, Aditya; Varshney, Pramod K. (Chapter, 2020)
      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 ...
    • Optimal scheduling of multiple spatio-temporally dependent observations using age-of-information 

      Håkansson, Victor Wattin; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Stefan (Chapter, 2020)
      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 ...
    • Optimal Scheduling Policy for Spatio-temporally Dependent Observations using Age-of-Information 

      Håkansson, Victor Wattin; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Stefan (Chapter, 2020)
      This paper proposes an optimal scheduling policy for a remote estimation problem, where sensor observations of two spatio-temporally correlated processes are broadcasted to two remote estimators. At each time instant only ...
    • Privacy-Preserving Distributed Learning with Nonsmooth Objective Functions 

      Gauthier, Francois; Gratton, Cristiano; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Stefan (Chapter, 2021)
      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 ...
    • Privacy-Preserving Distributed Maximum Consensus 

      Dasanadoddi Venkategowda, Naveen Kumar; Werner, Stefan (Peer reviewed; Journal article, 2020)
      We propose a privacy-preserving distributed maximum consensus algorithm where the local state of the agents and identity of the maximum state owner is kept private from adversaries. To that end, we reformulate the maximum ...
    • Privacy-preserving distributed precoder design for decentralized estimation 

      Dasanadoddi Venkategowda, Naveen Kumar; Werner, Stefan (Chapter, 2018)
      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 ...
    • Securing the Distributed Kalman Filter Against Curious Agents 

      Moradi, Ashkan; Dasanadoddi Venkategowda, Naveen Kumar; Talebi, Sayedpouria; Werner, Stefan (Chapter, 2021)
      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 ...