Flautim: A Federated Learning Platform using K8S and Flower - Barros, Pedro H. S. S.; Oliveira, Marcelo Q. A.; Orang, Omid; Silva, Felipe A. R. da; Erazo-Costa, Fabricio J.; Bastos, Allana Tavares; Silva, Petrônio C. L.; Santos, Glauber Soares dos; Loureiro, Antonio A. F.; Ravetti, Martín Gómez; Costa, Marcelo Azevedo; Guimarães, Frederico Gadelha; et al. (Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia); 2024: Anais Estendidos do XXX Simpósio Brasileiro de Sistemas Multimídia e WebDO - 10.5753/webmedia_estendido.2024.244009, 2024)
Federated learning (FL) is a cutting-edge technology in artificial intelligence that preserves data privacy and security while reducing the cost of computation and communication. It transforms traditional centralized machine learning and deep learning approaches to enable decentralized model training without the need for data exchange. This work presents Flautim, the first implementation of an FL platform in Brazil and all around Latin America based on Kubernetes (K8S) and Flower framework. Flautim is designed for academic use, enabling researchers without a technical background to conduct FL experiments on this platform easily. Also, this platform allows for the development of applications involving data gathered from connected vehicles. Thus, this study aims to introduce this new FL platform, providing comprehensive details of its architecture.