Privacy Preservation in Kubernetes-based Federated Learning: A Networking Approach

Abstract

Federated Learning (FL) is a distributed Machine Learning paradigm that enables multiple clients to collaboratively train a model under the control of a central server while preserving data locally in heterogeneous edge devices. To facilitate scalable deployment of FL systems, cloud computing and container-based approaches such as Kubernetes (K8s) have been recently proposed. K8s enables container orchestration for cloud and edge applications while reducing workload management complexity in FL ecosystems. Nonetheless, K8s can violate fundamental FL privacy principles, e.g., the inherent flat networking approach in K8s can potentially allow FL clients to access other client or domain resources. The latter poses an open research problem and gap in the literature because serious privacy risks can arise from attackers gaining access to any client in the FL setup. To address this problem, this paper presents a networking approach via network isolation at the link layer level, and authentication and data packet encryption at the network layer level. The former allows to create secure resource sharing, and the latter is used to protect in-transit data. For this purpose, we use a K8s networking operator and a secure network protocol suite. The above combination facilitates on-demand link-layer connectivity, per-link data source authentication, and confidentiality between FL actors. We tested our approach on a network testbed composed of different geo-located nodes where FL clients are deployed. Our promising results showcase the feasibility of the solution for privacy preservation at the network level in K8s-based FL.