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 . It addresses privacy risks via and 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.

中文翻译：

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kubeFlower：云边缘环境中基于 Kubernetes 的联邦学习的隐私保护框架

联合学习 (FL) 支持跨边缘设备的协作模型训练，同时在本地保存数据。由于设备异构性，部署 FL 面临挑战。使用 Kubernetes (K8s) 等云技术可以提供计算弹性，但可能会损害 FL 隐私原则。鉴于其扁平网络方法，K8s 可能会允许恶意 FL 客户端访问其他资源，从而危及 FL 隐私。本文介绍了隐私保护的K8s算子。它通过数据管理解决隐私风险。隔离保证资源安全共享，差异隐私保障个人数据隐私。我们引入了隐私保护持久卷声明者 (P3-VC​​)，它在管理隐私预算的同时增加了数据噪音。 kubeFlower 简化了 K8s 中的 FL 系统管理，同时确保隐私。我们在由不同地理位置的云和部署 FL 客户端的边缘节点组成的网络测试台上测试了我们的方法。与云边缘环境的基准相比，我们的结果证明了该方法在基于 K8s 的 FL 中保护隐私的有效性。