kubeFlower: A privacy-preserving framework for Kubernetes-based federated learning in cloud–edge environments

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)

Abstract

Federated Learning (FL) enables collaborative model training across edge devices while preserving data locally. Deploying FL faces challenges due to device heterogeneity. Using cloud technologies like Kubernetes (K8s) can offer computational elasticity, yet may compromise FL privacy principles. K8s can jeopardise FL privacy by potentially allowing malicious FL clients to access other resources given its flat networking approach. This paper introduces the privacy-preserving K8s operator kubeFlower. It addresses privacy risks via isolation-by-design and differential privacy for data management. Isolation ensures secure resource sharing, while differential privacy safeguards individual data privacy. We introduce the Privacy Preserving Persistent Volume Claimer (P3-VC), which adds noise to data while managing a privacy budget. kubeFlower simplifies FL system management in K8s while ensuring privacy. We tested our approach on a network testbed composed of different geo-located cloud and edge nodes where FL clients are deployed. Our results demonstrate the approach’s efficacy in preserving privacy in K8s-based FL compared to benchmarks for cloud–edge environments.
Original languageEnglish
Pages (from-to)558-572
Number of pages15
JournalFuture Generation Computer Systems
Volume157
Early online date4 Apr 2024
DOIs
Publication statusPublished - 1 Aug 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)

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