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.