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Federated Learning (FL) is a distributed Machine
Learning paradigm that allows multiple clients to collaboratively
train a model under the control of a central server while keeping
data locally in edge devices. To simplify workload management in
FL ecosystems, cloud computing and container-based approaches
such as Kubernetes (K8s) have been proposed for scalable de-
ployment. 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 network-
ing 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.


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