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

Arc-Consistency Algorithms on a Software DSM Platform

This work presents the parallelisation of the AC-5 arc-consistency algorithm for distributedshared memory platforms. We conducted our experiments using an adapted version of the PCSOS parallel system, over nite domains, running on top of Treadmarks, a software DSM system, on a cluster of 8 PCs connected via a Fast-Ethernet network. We ran four benchmarks used by the original PCSOS to debug and assess the performance of the system. Our results show that arc-consistency algorithms have a great potential for parallelisation on low cost distributed-shared memory platforms. One of the applications achieves superlinear speedups due to distributed labeling. Speedups for the other applications are limited by the write invalidate cache coherence protocol used by TreadMarks and extra synchronisation required by its memory consistency model, size of the problem, and kind of distribution of indexicals and labeling

Computational Mathematical Model Based on Lyapunov Function for the Hormonal Storage Control

Computational mathematical models have shown promise in the biological mechanism's reproduction. This work presents a computational mathematical model of the hormonal storage control applied to an endocrine cell. The model is based on a system of differential equations representing the internal cell dynamics and governed by the Lyapunov control function. Among the stages of these dynamics, we analyze the storage and degradation, which occur within some endocrine cells. The model’s evaluation considers, as an example, the synthesis–storage-release regulation of catecholamine in the adrenal medulla. Seven experiments, varying the input parameters, were performed to validate and evaluate the model. Different behaviors could be observed according to the numerical data used for future research and scientific contributions, besides confirming that Lyapunov control function is feasible to govern the cell dynamics