Multi-FedLS: a Framework for Cross-Silo Federated Learning Applications on Multi-Cloud Environments

2023/08/17 by Brum, Rafaela C., de Castro, Maria Clicia Stelling, Arantes, Luciana +2
#Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)

paper · doi:10.48550/arxiv.2308.08967

Abstract

Federated Learning (FL) is a distributed Machine Learning (ML) technique that can benefit from cloud environments while preserving data privacy. We propose Multi-FedLS, a framework that manages multi-cloud resources, reducing execution time and financial costs of Cross-Silo Federated Learning applications by using preemptible VMs, cheaper than on-demand ones but that can be revoked at any time. Our framework encloses four modules: Pre-Scheduling, Initial Mapping, Fault Tolerance, and Dynamic Scheduler. This paper extends our previous work \citebrum2022sbac by formally describing the Multi-FedLS resource manager framework and its modules. Experiments were conducted with three Cross-Silo FL applications on CloudLab and a proof-of-concept confirms that Multi-FedLS can be executed on a multi-cloud composed by AWS and GCP, two commercial cloud providers. Results show that the problem of executing Cross-Silo FL applications in multi-cloud environments with preemptible VMs can be efficiently resolved using a mathematical formulation, fault tolerance techniques, and a simple heuristic to choose a new VM in case of revocation.

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