2025-03-122025-03-122024BARBOSA, Lucas Nobre. Aprendizado federado baseado em múltiplas árvores de decisão para aplicações iot com computação de borda cooperativa. Orientador: André Figueira Riker. 2024. 15 f. Trabalho de Curso (Bacharelado em Ciência da Computação) – Faculdade de Computação, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, 2024 Disponível em: https://bdm.ufpa.br/jspui/handle/prefix/7836. Acesso em:.https://bdm.ufpa.br/jspui/handle/prefix/7836Internet of Things (IoT) have relied on edge computing nodes to de centralize computation and to bring more processing power near the IoT devi ces, such as sensors and actuators. IoT edge computing nodes have more data processing power and energy resources than regular IoT devices that aim to mo nitor and actuate on the environment. However, in general, IoT edge computing nodes are not designed for intensive Machine Learning (ML) training or to host large ML models. In the current IoT network architectures, there are multiple IoT edge computing nodes strategically located near a large number of IoT de vices, where each of the IoT edge computing node has access to part of the data produced by the whole IoT network. In this scenario, each IoT edge computing node runs lightweight ML models in its local dataset. In this paper, we propose a solution, called FEDT (FEderated Decision Tree), that aggregates the learning produced by multiple decision trees from cooperative IoT edge nodes, following the federated learning principles. We present four different federated learning strategies and demonstrate that FEDT can achieve around 80% of a centralized MLmodel in terms of Pearson correlationAcesso AbertoComputação de bordaAprendizado de máquinaAprendizado federadoPreservação da privacidade de dadosCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOTrabalho de Curso - Graduação - Monografia