• 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 ...
    • Continual local updates for federated learning with enhanced robustness to link noise 

      Lari, Ehsan; Gogineni, Vinay Chakravarthi; Arablouei, Reza; Werner, Anders Stefan (Chapter, 2023)
      Communication errors caused by noisy links can negatively impact the accuracy of federated learning (FL) algorithms. To address this issue, we introduce an FL algorithm that is robust to communication errors while concurrently ...
    • Distributed Learning over Networks with Non-Smooth Regularizers and Feature Partitioning 

      Gratton, Cristiano; Kumar Dasanadoddi Venkategowda, Naveen; Arablouei, Reza; Werner, Stefan (Chapter, 2021)
      We develop a new algorithm for distributed learning with non-smooth regularizers and feature partitioning. To this end, we transform the underlying optimization problem into a suitable dual form and solve it using the ...
    • 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 ...
    • Privacy-Preserved Distributed Learning With Zeroth-Order Optimization 

      Gratton, Cristiano; Kumar Dasanadoddi Venkategowda, Naveen; Arablouei, Reza; Werner, Stefan (Peer reviewed; Journal article, 2022)
      We develop a privacy-preserving distributed algorithm to minimize a regularized empirical risk function when the first-order information is not available and data is distributed over a multi-agent network. We employ a ...
    • Privacy-preserving distributed machine learning for artificial intelligence of things 

      Gratton, Cristiano (Doctoral theses at NTNU;2023:12, Doctoral thesis, 2023)
      This thesis proposes machine learning algorithms that can be fully distributed over ad-hoc networks of machines/agents. Developing distributed algorithms for artificial intelligence is necessary since running machine-lea ...
    • Resource-efficient federated learning robust to communication errors 

      Lari, Ehsan; Gogineni, Vinay Chakravarthi; Arablouei, Reza; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)
      The effectiveness of federated learning (FL) in leveraging distributed datasets is highly contingent upon the accuracy of model exchanges between clients and servers. Communication errors caused by noisy links can negatively ...