Privacy Preserving Federated RSRP Estimation for Future Mobile Networks

Abstract

Leveraging location information for machine learning applications in mobile networks is challenging due to the distributed nature of the data and privacy concerns. Federated Learning (FL) helps to tackle these issues and is a big step towards enabling privacy-aware distributed model training; however still prone to sophisticated privacy attacks such as membership inference. In this paper, we implement an FL approach to estimate Reference Signal Received Power (RSRP) values using geographical location information of the user equipment. We propose a privacy-preserving mechanism using differential privacy to protect against privacy attacks and demonstrate the impacts and the privacy-utility trade-off via privacy accounting measures.