@inproceedings{a139f93fdfd144c99d5cb6ccc6c50d55,
title = "FLORAS: Differentially Private Wireless Federated Learning Using Orthogonal Sequences",
abstract = "We propose a novel private-preserving uplink over-the-air computation (AirComp) method, termed FLORAS, for wireless federated learning (FL) systems. From the communication design perspective, FLORAS eliminates the requirement of channel state information at the transmitters (CSIT) by leveraging the properties of orthogonal sequences. From the privacy perspective, we prove that FLORAS can offer pure differential privacy (DP) guarantee, and explicitly characterize the achievable ϵ-DP level as a function of the FLORAS parameter configuration. A novel FL convergence bound is derived which, combined with the pure DP guarantee, allows for a smooth tradeoff between convergence rate and DP guarantee levels. Experiments based on real-world datasets not only corroborate the theoretical findings but also empirically demonstrate the communication and privacy advantages of FLORAS over state-of-the-art AirComp methods.",
author = "Xizixiang Wei and Tianhao Wang and Ruiquan Huang and Cong Shen and Jing Yang and Poor, {H. Vincent}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Communications, ICC 2023 ; Conference date: 28-05-2023 Through 01-06-2023",
year = "2023",
doi = "10.1109/ICC45041.2023.10278611",
language = "English (US)",
series = "IEEE International Conference on Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3121--3126",
editor = "Michele Zorzi and Meixia Tao and Walid Saad",
booktitle = "ICC 2023 - IEEE International Conference on Communications",
address = "United States",
}