Browsing NTNU Open by Author "Werner, Anders Stefan"

Now showing items 1-12 of 12

    • Asynchronous online federated learning with reduced communication requirements 

      Gauthier, Francois Jean Rene; Gogineni, Vinay Chakravarthi; Werner, Anders Stefan; Huang, Yih-Fang; Kuh, Anthony (Peer reviewed; Journal article, 2023)
      Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers a best-case scenario regarding the ...

    • Autoencoder-based hyperspectral anomaly detection using kernel principal component pre-processing 

      Müller, Katinka; Gogineni, Vinay Chakravarthi; Orlandic, Milica; Werner, Anders Stefan (Chapter, 2023)
      Anomaly detection in hyperspectral remote sensing applications has attracted colossal attention due to its ability to uncover small distinctive objects dispersed across large geographical areas in an unsupervised manner. ...

    • Communication-efficient and privacy-aware distributed learning 

      Gogineni, Vinay Chakravarthi; Moradi, Ashkan; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)
      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 ...

    • 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 ...

    • Decentralized Graph Federated Multitask Learning for Streaming Data 

      Gogineni, Vinay Chakravarthi; Werner, Anders Stefan; Huang, Yih-Fang; Kuh, Anthony (Annual Conference on Information Sciences and Systems (CISS);56, Chapter, 2022)
      In federated learning (FL), multiple clients connected to a single server train a global model based on locally stored data without revealing their data to the server or other clients. Nonetheless, the current FL architecture ...

    • Distributed quantile regression with non-convex sparse penalties 

      Mirzaeifard, Reza; Gogineni, Vinay Chakravarthi; Kumar Dasanadoddi Venkategowda, Naveen; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)
      The surge in data generated by IoT sensors has increased the need for scalable and efficient data analysis methods, particularly for robust algorithms like quantile regression, which can be tailored to meet a variety of ...

    • Networked personalized federated learning using reinforcement learning 

      Gauthier, Francois Jean Rene; Gogineni, Vinay Chakravarthi; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)
      Personalized federated learning enables every edge device or group of edge devices within the distributed network to learn a device- or cluster-specific model tailored to their local needs. Data scarcity, however, makes ...

    • Personalized graph federated learning with differential privacy 

      Gauthier, Francois Jean Rene; Gogineni, Vinay Chakravarthi; Werner, Anders Stefan; Huang, Yih-Fang; Kuh, Anthony (Peer reviewed; Journal article, 2023)
      This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining ...

    • 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 ...

    • Robust Networked Federated Learning for Localization 

      Mirzaeifard, Reza; Venkategowda, Naveen K. D.; Werner, Anders Stefan (Chapter, 2023)
      This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices. Due to the decentralized nature of federated ...

    • Robust phase retrieval with non-convex penalties 

      Mirzaeifard, Reza; Dasanadoddi Venkategowda, Naveen Kumar; Werner, Anders Stefan (Journal article, 2023)
      This paper proposes an alternating direction method of multiplier (ADMM) based algorithm for solving the sparse robust phase retrieval with non-convex and non-smooth sparse penalties, such as minimax concave penalty (MCP). ...

    • Total Variation-Based Distributed Kalman Filtering for Resiliency Against Byzantines 

      Moradi, Ashkan; Venkategowda, Naveen K. D.; Werner, Anders Stefan (Peer reviewed; Journal article, 2023)
      This article proposes a distributed Kalman filter (DKF) with enhanced robustness against Byzantine adversaries. A Byzantine agent is a legitimate network agent that, unlike an honest agent, manipulates information before ...