Towards Energy-Aware Federated Traffic Prediction for Cellular Networks

Author:

Perifanis Vasileios1,Pavlidis Nikolaos1,Yilmaz Selim F.2,Wilhelmi Francesc3,Guerra Elia4,Miozzo Marco4,Efraimidis Pavlos S.1,Dini Paolo4,Koutsiamanis Remous-Aris5

Affiliation:

1. Democritus University of Thrace,Department of Electrical and Computer Engineering,Xanthi,Greece

2. Imperial College London,Department of Electrical and Electronic Engineering,London,United Kingdom

3. Radio Systems Research, Nokia Bell Labs,Stuttgart,Germany

4. Sustainable Artificial Intelligence, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA),Barcelona,Spain

5. IMT Atlantique, Inria,LS2N,Department of Automation, Production and Computer Sciences,Nantes,France

Publisher

IEEE

Reference31 articles.

1. Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

2. Federated learning for 5g base station traffic forecasting;perifanis;Computer Networks,2023

3. Communication-Efficient Learning of Deep Networks from Decentral-ized Data;mcmahan;Proceedings of the 20th AISTATS,2017

4. Estimating Energy Consumption of Cloud, Fog and Edge Computing Infrastructures

5. Attention is all you need;vaswani;Advances in neural information processing systems,2017

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