Browsing NTNU Open by Author "Kuh, Anthony"

Now showing items 1-8 of 8

    • 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 ...

    • Communication-Efficient Online Federated Learning Framework for Nonlinear Regression 

      Gogineni, Vinay Chakravarthi; Werner, Stefan; Huang, Yih-Fang; Kuh, Anthony (Peer reviewed; Journal article, 2022)
      Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced a framework for online FL (Online-Fed) ...

    • Communication-Efficient Online Federated Learning Strategies for Kernel Regression 

      Gogineni, Vinay Chakravarthi; Werner, Stefan; Huang, Yih-Fang; Kuh, Anthony (Journal article; Peer reviewed, 2022)

    • 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 ...

    • Nonlinear adaptive filtering with kernel set-membership approach 

      Chen, Kewei; Werner, Stefan; Huang, Yih-Fang; Kuh, Anthony (Peer reviewed; Journal article, 2020)
      This paper develops nonlinear kernel adaptive filtering algorithms based on the set-membership filtering (SMF) framework. The set-membership-based filtering approach is distinct from the conventional adaptive filtering ...

    • 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 ...

    • Personalized Online Federated Learning for IoT/CPS: Challenges and Future Directions 

      Gogineni, Vinay Chakravarthi; Werner, Stefan; Gauthier, Francois; Huang, Yih-Fang; Kuh, Anthony (Journal article, 2022)
      In recent years, federated learning (FL) has emerged as a powerful paradigm for distributed learning thanks to its privacy-preserving capabilities. With the use of FL, a network of edge devices can make intelligent decisions ...

    • Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression 

      Gauthier, Francois; Gogineni, Vinay Chakravarthi; Werner, Stefan; Huang, Yih-Fang; Kuh, Anthony (Chapter, 2022)
      Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated ...