Privacy preserving federated RSRP estimation for future mobile networks

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 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.
Research paper

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.

Full abstract in IEEEXplore DOI: 10.1109/GCWkshps52748.2021.9682084


Omer Haliloglu, Elif Ustundag Soykan, Abdulrahman Alabbasi – Ericsson Research

Published in proceedings of 2021 IEEE Globecom Workshops (GC Wkshps), Dec 7, 2021

©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse.