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  1. ieeexplore.ieee.org

    The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient descent (SGD) to perform distributed learning and ...
    Author:Paolo Di Lorenzo, Claudio Battiloro, Mattia Merluzzi, Sergio BarbarossaPublished:2021
  2. researchgate.net

    PDF | On Jun 6, 2021, Paolo Di Lorenzo and others published Dynamic Resource Optimization for Adaptive Federated Learning at the Wireless Network Edge | Find, read and cite all the research you ...
  3. researchgate.net

    chine learning at the wireless network edge. IndexTerms— Adaptive federated learning, Lyapunov stochas- tic optimization, edge machine learning, resource allocation.
  4. ieeexplore.ieee.org

    The aim of this work is to propose a novel dynamic resource allocation strategy for adaptive Federated Learning (FL), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs). Due to time-varying wireless channel conditions, communication resources (e.g., set of transmitting devices, transmit powers, bits), computation parameters (e.g., CPU cycles at devices ...
  5. semanticscholar.org

    A novel dynamic resource allocation strategy for energy-efficient federated learning at the wireless network edge, with latency and learning performance guarantees, based on Lyapunov stochastic optimization tools. The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient federated learning at the wireless network edge, with latency and learning ...
  6. iris.uniroma1.it

    The aim of this work is to propose a novel dynamic resource al-location strategy for adaptive Federated Learning (FL), in the con-text of beyond 5G networks endowed with Recongurable Intelli-gent Surfaces (RISs). Due to time-varying wireless channel con-ditions, communication resources (e.g., set of transmitting devices,
  7. iris.uniroma1.it

    Abstract—The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient adaptive feder-ated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs ...
  8. Dynamic Resource Allocation for Federated Learning Lyapunov Optimization Virtual Queues: Z tfor the Latency inequality constraint: Z t+1 = max n 0,Z t+ ϵ z L t−L o Q tfor the accuracy inequality constraint Q t+1 = max n 0,Q t+ ϵ q G −Gb t o Y tfor the convergence rate equality constraint: Y t+1 = [Y t+ ϵ y,t(αb t−α)] ·I Gb t≤G ...
  9. Adaptive Resource Optimization for Edge Inference with Goal-Oriented Communications December 20, 2022. Lyapunov-based optimization of edge resources for energy-efficient adaptive federated learning December 20, 2022. Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing December 20, 2022 ... Webinar "5G Network ...

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