Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks

Abstract:Deep a promising solution to improve spectrum techniques by utilizing -driven approaches to manage share limited spectrum resources for applications. For several of these applications, the sensitive wireless data (such as spectrograms) are stored in a shared or multistakeholder and are therefore prone to . This paper aims to address such

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