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.

翻译：联邦学习（Federated Learning, FL）是一种分布式机器学习（Machine Learning, ML）技术，能够利用云环境的同时保护数据隐私。我们提出Multi-FedLS框架，该框架通过使用抢占式虚拟机（preemptible VMs）管理多云资源，降低跨数据孤岛联邦学习应用的执行时间与财务成本。此类虚拟机虽成本低于按需实例，但随时可能被回收。本框架包含四个模块：预调度模块、初始映射模块、容错模块与动态调度模块。本文在先前工作\cite{brum2022sbac}的基础上，正式描述了Multi-FedLS资源管理器框架及其各模块。我们在CloudLab上使用三个跨数据孤岛FL应用开展实验，概念验证表明Multi-FedLS可在由AWS与GCP（两大商业云服务提供商）构成的多云环境中运行。结果表明，通过数学建模、容错技术及在虚拟机回收时选择新实例的简单启发式方法，可高效解决在多云环境中使用抢占式虚拟机执行跨数据孤岛FL应用的问题。