Bravalheri, Anderson C

Gonzalez, Luis F.

Hussain, Rasheed

Nejabati, Reza

Parra Ullauri, Juan Marcelo

Simeonidou, Dimitra

Valera, Francisco

Vasilakos, Xenofon

Vidal, Ivan

English

Federated Learning (FL) is a distributed MachineLearning paradigm that allows multiple clients to collaborativelytrain a model under the control of a central server while keepingdata locally in edge devices. To simplify workload management inFL ecosystems, cloud computing and container-based approachessuch as Kubernetes (K8s) have been proposed for scalable de-ployment. Nonetheless, K8s can violate fundamental FL privacyprinciples, e.g., the inherent flat networking approach in K8scan potentially allow FL clients to access other client or domainresources. The latter poses an open research problem and gapin the literature because serious privacy risks can arise fromattackers gaining access to any client in the FL setup. To addressthis problem, this paper presents a networking approach vianetwork isolation at the link layer level, and authentication anddata packet encryption at the network layer level. The formerallows to create secure resource sharing, and the latter is used toprotect in-transit data. For this purpose, we use a K8s network-ing operator and a secure network protocol suite. The abovecombination facilitates on-demand link-layer connectivity, per-link data source authentication, and confidentiality between FLactors. We tested our approach on a network testbed composedof different geo-located nodes where FL clients are deployed.Our promising results showcase the feasibility of the solution forprivacy preservation at the network level in K8s-based FL

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

https://research-information.bris.ac.uk/files/357963251/Privacy_Preservation_in_Kubernetes_based_Federated_Learning_A_Networking_Approach.pdf

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

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