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

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 Computin

Towards a Federated Learning Framework on a Multi-Cloud Environment

International audienceThis paper proposes Multi-FedLS, a Cross-silo Federated Learning (FL) framework for a multi-cloud environment aiming at minimizing financial cost as well as execution time. It comprises four modules: Pre-Scheduling, Initial Mapping, Fault Tolerance, and Dynamic Scheduler. Given an application and a multi-cloud environment, the Pre-Scheduling module runs experiments to obtain the expected execution times of the FL tasks and communication delays. The Initial Mapping module receives these computed values and provides a scheduling map for the server and clients' VMs. Finally, Multi-FedLS deploys the selected VMs, starts the FL application, and monitors it. The Fault Tolerance (FT) module includes fault tolerance strategies in the FL application, such as checkpoint and replication, and detects some anomalous behaviors. In case of an unexpected increase in the communication delay or a VM failure, the FT module triggers the Dynamic Scheduler Module in order to select a new VM and resume the concerned tasks of the FL application. Some preliminary experiments are presented, confirming that some proposed strategies are crucial to efficiently execute an FL application on a multi-cloud environment

Optimizing Execution Time and Costs of Cross-Silo Federated Learning Applications with Datasets on different Cloud Providers

International audienceUnder the coordination of a central server, Federate Learning (FL) enables a set of clients to collaboratively train a global machine learning model without exchanging their local data. When such clients have powerful machines, it is called cross-silo FL, and they store their data in private repositories denoted silos. We are interested in this paper in cross-silo FL where silos are geographically located in different regions of multi-cloud providers. Thus, aiming at minimizing financial costs and execution times of a cross-silo FL application, we propose a model based on a scheduling problem mathematical formulation, which receives as input both the application parameters and the cloud providers' resource features where clients' data are stored and renders the best assignment of clients and server to virtual machines. This formulation is part of a framework proposal to execute FL applications in different cloud providers. Taking as a use case a Tumor-Infiltrating Lymphocytes Classification problem, an FL application whose clients' datasets spread over different cloud providers' data repositories, evaluation results show that our model is scalable and improves the execution time and financial costs of the FL application by up to 53.70% and 48.34% in a scenario with 50 clients, executing in around 200 seconds, when compared to results where VMs are randomly selected. Experimental results with client silos in different Google (GCP) and Amazon (AWS) cloud regions also confirmed the effectiveness of our proposed model in a real multi-cloud environment

Receptor of ghrelin is expressed in cutaneous neurofibromas of individuals with neurofibromatosis 1

Abstract Background Multiple cutaneous neurofibromas are a hallmark of neurofibromatosis 1 (NF1). They begin to appear during puberty and increase in number and volume during pregnancy, suggesting a hormonal influence. Ghrelin is a hormone that acts via growth hormone secretagogue receptor (GHS-R), which is overexpressed in many neoplasms and is involved in tumorigenesis. We aimed to investigate GHS-R expression in NF1 cutaneous neurofibromas and its relationship with tumors volume, and patient’s age and gender. Results Sample comprised 108 cutaneous neurofibromas (55 large and 53 small tumors) from 55 NF1 individuals. GHS-R expression was investigated by immunohistochemistry in tissue micro and macroarrays and quantified using a digital computer-assisted method. All neurofibromas expressed GHS-R, with a percentage of positive cells ranging from 4.9% to 76.1%. Large neurofibromas expressed more GHS-R than the small ones. The percentage of GHS-R-positive cells and intensity of GHS-R expression were positively correlated with neurofibromas volume. GHS-R expression was more common in female gender. Conclusions GHS-R is expressed in cutaneous neurofibromas. Larger neurofibromas have a higher percentage of positive cells and higher GHS-R intensity. Based on our results we speculate that ghrelin may have an action on the tumorigenesis of cutaneous neurofibromas. Future studies are required to understand the role of ghrelin in the pathogenesis of NF1-associated cutaneous neurofibroma

A Etnografia em Questão

Este artigo tem por objetivo analisar o “fazer etnográfico”. Ele é fruto de um ciclo, denominado Etnografando I, desenvolvido pelo NECON/UFSM (Núcleo de estudos Contemporâneos), de março a abril de 2006. A etnografia, enquanto método de pesquisa e modelo de apreensão da realidade é debatida por meio da análise de autores como: Malinowski, Clifford Geertz, James Cliffors e Roberto Cardoso de Oliveira. Trata-se de uma tentativa de salientar o quanto a etnografia é uma atividade complexa e quais os elementos nela inseridos, da pesquisa empírica à escrita dos dados in loco e, posteriormente, quando da produção cientifica sobre os mesmos