Um Estudo Comparativo de Frameworks de Aprendizado Federado para Cidades Inteligentes baseadas em Internet das Coisas
DOI:
https://doi.org/10.5433/1679-0375.2026.v47.53937Palavras-chave:
cidades inteligentes, internet das coisas, aprendizado de máquina, aprendizado federado, privacidadeResumo
A integração de Aprendizado de Máquina (AM) com cidades inteligentes oferece soluções promissoras para a análise de dados de Internet of Things (IoT). No entanto, abordagens convencionais de AM frequentemente enfrentam dificuldades para equilibrar acurácia e custo computacional, especialmente em ambientes de IoT heterogêneos e com restrições de recursos. O Aprendizado Federado (AF) surge como uma alternativa, permitindo o aprendizado colaborativo com preservação de privacidade, ao mesmo tempo em que reduz a necessidade de coleta centralizada de dados. Na última década, tanto a indústria quanto a academia contribuíram para um ecossistema crescente de frameworks de AF, com diferentes funcionalidades e escolhas de \textit{design}. Apesar desse avanço, a pesquisa sobre AF em cidades inteligentes ainda é limitada, dificultando a avaliação da adequação dos frameworks para aplicações reais. Este artigo apresenta uma análise comparativa de \textit{frameworks} de AF em cenários de cidades inteligentes. Avaliamos frameworks representativos, com foco em Flower e NVFlare, utilizando conjuntos de dados derivados de infraestruturas de IoT. Os resultados evidenciam os equilíbrios entre ganhos e limitações do AF em comparação às abordagens convencionais de AM, oferecendo subsídios para pesquisadores, engenheiros e gestores.
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