On differential privacy for federated learning in wireless systems with multiple base stations

In 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 behavi...

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Bibliographic Details
Published in:IET communications Vol. 18; no. 20; pp. 1853 - 1867
Main Authors: Tavangaran, Nima, Chen, Mingzhe, Yang, Zhaohui, Da Silva, José Mairton B., Poor, H. Vincent
Format: Journal Article
Language:English
Published: Wiley 01.12.2024
Subjects:
Computer Science with specialization in Computer Communication
data privacy
Datavetenskap med inriktning mot datorkommunikation
differential privacy
federated learning
optimization
scheduling
wireless channels
wireless communications
ISSN:1751-8628, 1751-8636, 1751-8636
Online Access:Get full text
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Summary:In 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. Here, 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 and analyse the performance of such a system.
ISSN:1751-8628
1751-8636
1751-8636
DOI:10.1049/cmu2.12722