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Federated Learning (FL) is a distributed Machine Learning (ML) technique thatcan benefit from cloud environments while preserving data privacy. We proposeMulti-FedLS, a framework that manages multi-cloud resources, reducing executiontime and financial costs of Cross-Silo Federated Learning applications by usingpreemptible VMs, cheaper than on-demand ones but that can be revoked at anytime. Our framework encloses four modules: Pre-Scheduling, Initial Mapping,Fault Tolerance, and Dynamic Scheduler. This paper extends our previous work\cite{brum2022sbac} by formally describing the Multi-FedLS resource managerframework and its modules. Experiments were conducted with three Cross-Silo FLapplications on CloudLab and a proof-of-concept confirms that Multi-FedLS canbe executed on a multi-cloud composed by AWS and GCP, two commercial cloudproviders. Results show that the problem of executing Cross-Silo FLapplications in multi-cloud environments with preemptible VMs can beefficiently resolved using a mathematical formulation, fault tolerancetechniques, and a simple heuristic to choose a new VM in case of revocation.

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