2024
DOI: 10.1109/ojcoms.2024.3378266
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A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks

Sree Krishna Das,
Benoit Champagne,
Ioannis Psaromiligkos
et al.

Abstract: Wireless networks are increasingly relying on machine learning (ML) paradigms to provide various services at the user level. Yet, it remains impractical for users to offload their collected data set to a cloud server for centrally training their local ML model. Federated learning (FL), which aims to collaboratively train a global ML model by leveraging the distributed wireless computation resources across users without exchanging their local information, is therefore deemed as a promising solution for enabling… Show more

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