تقرير
Multi-FedLS: a Framework for Cross-Silo Federated Learning Applications on Multi-Cloud Environments
| العنوان: | Multi-FedLS: a Framework for Cross-Silo Federated Learning Applications on Multi-Cloud Environments |
|---|---|
| المؤلفون: | Brum, Rafaela C., de Castro, Maria Clicia Stelling, Arantes, Luciana, Drummond, Lúcia Maria de A., Sens, Pierre |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Distributed, Parallel, and Cluster Computing |
| الوصف: | 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 \cite{brum2022sbac} 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. Comment: In review by Journal of Parallel and Distributed Computing |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2308.08967 |
| رقم الأكسشن: | edsarx.2308.08967 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |