On differential privacy for federated learning in wireless systems with multiple base stations
-
Published:2024-01-17
Issue:
Volume:
Page:
-
ISSN:1751-8628
-
Container-title:IET Communications
-
language:en
-
Short-container-title:IET Communications
Author:
Tavangaran Nima1ORCID,
Chen Mingzhe2,
Yang Zhaohui3,
Da Silva José Mairton B.4ORCID,
Poor H. Vincent1
Affiliation:
1. Department of Electrical and Computer Engineering Princeton University Princeton New Jersey USA
2. Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables Florida USA
3. College of Information Science and Electronic Engineering Zhejiang University Hangzhou China
4. Department of Information Technology Uppsala University Uppsala Sweden
Abstract
AbstractIn this work, we consider a federated learning model in a wireless system with multiple base stations and inter‐cell interference. We apply a differentially private scheme to transmit information from users to their corresponding base station during the learning phase. We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap. Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage. To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. We then extend this scheme to reduce the total privacy leakage by optimizing the differential privacy artificial noise. We apply the solutions of these two procedures as parameters of a federated learning system where each user is equipped with a classifier and communication cells have mostly fewer resource blocks than numbers of users. The simulation results show that our proposed scheduler improves the average accuracy of the predictions compared with a random scheduler. In particular, the results show an improvement of over 6%. Furthermore, its extended version with noise optimizer significantly reduces the amount of privacy leakage.
Funder
Deutsche Forschungsgemeinschaft
National Science Foundation
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Science Applications
Reference52 articles.
1. Machine Learning and Wireless Communications
2. Wireless for Machine Learning: A Survey
3. Bonawitz K. Eichner H. Grieskamp W. et al.:Towards federated learning at scale: System design. In:Proc. Syst. Mach. Learn. Conf. pp. 1–15(2019)
4. Konečný J. McMahan H.B. Ramage D. Richtárik P.:Federated optimization: Distributed machine learning for on‐device intelligence.CoRR(2016).http://arxiv.org/abs/1610.02527
5. McMahan B. Moore E. Ramage D. et al.:Communication‐efficient learning of deep networks from decentralized data. In:Proceedings of the International Conference on Artificial Intelligence and Statistics pp. 1273–1282.PMLR(2017)