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Browsing by Subject "Federated learning"

Now showing items 1-7 of 7

    • Blockchained Adaptive Federated Auto MetaLearning BigData and DevOps CyberSecurity Architecture in Industry 4.0 

      Demertzis, K., I; adis, L., P; enidis, E., T; ritas, N., K; iri, M., K; iras, P. (2021)
      Maximizing the production process in modern industry, as proposed by Industry 4.0, requires extensive use of Cyber-Physical Systems (CbPS). Artificial intelligence technologies, through CbPS, allow monitoring of natural ...

    • D2D-assisted federated learning in mobile edge computing networks 

      Zhang X., Liu Y., Liu J., Argyriou A., Han Y. (2021)
      With the proliferation of edge intelligence and the breakthroughs in machine learning, Federated Learning (FL) is capable of learning a shared model across several edge devices by preserving their private data from being ...

    • An explainable semi-personalized federated learning model 

      Demertzis K., Iliadis L., Kikiras P., Pimenidis E. (2022)
      Training a model using batch learning requires uniform data storage in a repository. This approach is intrusive, as users have to expose their privacy and exchange sensitive data by sending them to central entities to be ...

    • Federated Learning Protocols for IoT Edge Computing 

      Foukalas F., Tziouvaras A. (2022)
      In this article, we provide a set of federated learning (FL) protocols for future Internet architectures, which integrate the edge computing with the Internet of Things (IoT) known as 'IoT edge computing.' The proposed ...

    • Local & Federated Learning at the network edge for efficient predictive analytics 

      Harth N., Anagnostopoulos C., Voegel H.-J., Kolomvatsos K. (2022)
      The ability to perform computation on devices present in the Internet of Things (IoT) and Edge Computing (EC) environments leads to bandwidth, storage, and energy constraints, as most of these devices are limited with ...

    • An Overview of Enabling Federated Learning over Wireless Networks 

      Foukalas F., Tziouvaras A., Tsiftsis T.A. (2021)
      In this paper, we provide an overview of enabling federated learning (FL) techniques over wireless networks. More specifically, we present key techniques such as model compression, quantization and sparsification that ...

    • Towards Efficient Decentralized Federated Learning 

      Pappas C., Papadopoulos D., Chatzopoulos D., Panagou E., Lalis S., Vavalis M. (2022)
      We focus on the problem of efficiently deploying a federated learning training task in a decentralized setting with multiple aggregators. To that end, we introduce a number of improvements and modifications to the recently ...