Aprendizado Federado baseado em Múltiplas Árvores de Decisão para Computação de Borda IoT Cooperativa
Resumo
A Internet das Coisas (IoT) tem dependido de nós de computação em borda para descentralizar a computação e trazer mais poder de processamento próximo aos dispositivos IoT, como sensores e atuadores. Os nós de computação de borda da IoT têm mais poder de processamento de dados e recursos energéticos do que os dispositivos IoT regulares que visam monitorar e atuar no ambiente. No entanto, em geral, os nós de computação de borda não são projetados para treinamento intensivo de Aprendizado de Máquina (ML) ou para hospedar grandes modelos de ML. Nas arquiteturas de rede IoT atuais, existem múltiplos nós de computação de borda estrategicamente localizados perto de um grande número de dispositivos, onde cada um dos nós de computação de borda tem acesso a parte dos dados produzidos por toda a rede IoT. Neste cenário, cada nó de computação de borda executa modelos de ML leves em seu conjunto de dados local. Neste artigo, propomos uma solução, chamada FEderated Decision Tree (FEDT), Árvore de Decisão Federada, que agrega o aprendizado produzido por múltiplas árvores de decisão de nós de borda cooperativos, seguindo os princípios de aprendizado federado. Apresentamos quatro estratégias de aprendizado federado diferentes e demonstramos que o FEDT pode alcançar cerca de 80% de um modelo de ML centralizado em termos de correlação de Pearson.
Palavras-chave:
Aprendizado Federado, IoT, Árvore de Decisão
Referências
Berman, J. J. (2016). Chapter 4 - Understanding your data. In Berman, J. J. (Ed.), Data Simplification (pp. 135–187). Morgan Kaufmann.
Candanedo, L. M., Feldheim, V., & Deramaix, D. (2017). Data driven prediction models of energy use of appliances in a low-energy house. Energy and Buildings, 140, 81–97.
Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems, 35, 507–520.
Hauschild, A.-C., Lemanczyk, M., Matschinske, J., Frisch, T., Zolotareva, O., Holzinger, A., Baumbach, J., & Heider, D. (2022). Federated Random Forests can improve local performance of predictive models for various healthcare applications. Bioinformatics, 38(8), 2278–2286.
Huang, P., Li, D., & Yan, Z. (2023). Wireless federated learning with asynchronous and quantized updates. IEEE Communications Letters, 27(9), 2393–2397.
Ji, Y., & Chen, L. (2023). Fedqnn: A computation–communication-efficient federated learning framework for IoT with low-bitwidth neural network quantization. IEEE Internet of Things Journal, 10(3), 2494–2507.
Leite, L., Santo, Y., Dalmazo, B. L., & Riker, A. (2024). Federated learning under attack: Improving gradient inversion for batch of images. arXiv preprint arXiv:2409.17767.
Lindskog, W., & Prehofer, C. (2023). A federated learning benchmark on tabular data: Comparing tree-based models and neural networks. In 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC) (pp. 239–246).
Markovic, T., Leon, M., Buffoni, D., & Punnekkat, S. (2022). Random forest based on federated learning for intrusion detection. In Maglogiannis, I., Iliadis, L., Macintyre, J., & Cortez, P. (Eds.), Artificial Intelligence Applications and Innovations (pp. 132–144). Cham: Springer International Publishing.
Maseer, Z. K., Yusof, R., Bahaman, N., Mostafa, S. A., & Foozy, C. F. M. (2021). Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset. IEEE Access, 9, 22351–22370.
Nettleton, D. (2014). Chapter 6 - Selection of variables and factor derivation. In Nettleton, D. (Ed.), Commercial Data Mining (pp. 79–104). Morgan Kaufmann.
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konecny, J., Kumar, S., & McMahan, H. B. (2020). Adaptive federated optimization. arXiv preprint arXiv:2003.00295.
Riker, A., Mota, R., Rosário, D., Pereira, V., & Curado, M. (2022). Autonomic management of group communication for Internet of Things applications. International Journal of Communication Systems, 35(11), e5200.
Santo, Y., Abelém, A., Riker, A., & Tsiropoulou, E. E. (2025). Integrating data privacy and energy management in smart cities with partial sustainable IoT networks. IEEE Access.
Shen, T., Mishra, C. S., Sampson, J., Kandemir, M. T., & Narayanan, V. (2022). An efficient edge-cloud partitioning of random forests for distributed sensor networks. IEEE Embedded Systems Letters.
Silva, M., Riker, A., Torrado, J., Santos, J., & Curado, M. (2021). Extending energy neutral operation in Internet of Things. IEEE Internet of Things Journal, 9(10), 7510–7524.
Singhal, P., Pandey, S. R., & Popovski, P. (2024). Greedy Shapley client selection for communication-efficient federated learning. IEEE Networking Letters, 1–1.
Ullah, A., Anwar, S. M., Li, J., Nadeem, L., Mahmood, T., Rehman, A., & Saba, T. (2024). Smart cities: The role of Internet of Things and machine learning in realizing a data-centric smart environment. Complex & Intelligent Systems, 10(1), 1607–1637.
Yu, R., & Li, P. (2021). Toward resource-efficient federated learning in mobile edge computing. IEEE Network, 35(1), 148–155.
Candanedo, L. M., Feldheim, V., & Deramaix, D. (2017). Data driven prediction models of energy use of appliances in a low-energy house. Energy and Buildings, 140, 81–97.
Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems, 35, 507–520.
Hauschild, A.-C., Lemanczyk, M., Matschinske, J., Frisch, T., Zolotareva, O., Holzinger, A., Baumbach, J., & Heider, D. (2022). Federated Random Forests can improve local performance of predictive models for various healthcare applications. Bioinformatics, 38(8), 2278–2286.
Huang, P., Li, D., & Yan, Z. (2023). Wireless federated learning with asynchronous and quantized updates. IEEE Communications Letters, 27(9), 2393–2397.
Ji, Y., & Chen, L. (2023). Fedqnn: A computation–communication-efficient federated learning framework for IoT with low-bitwidth neural network quantization. IEEE Internet of Things Journal, 10(3), 2494–2507.
Leite, L., Santo, Y., Dalmazo, B. L., & Riker, A. (2024). Federated learning under attack: Improving gradient inversion for batch of images. arXiv preprint arXiv:2409.17767.
Lindskog, W., & Prehofer, C. (2023). A federated learning benchmark on tabular data: Comparing tree-based models and neural networks. In 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC) (pp. 239–246).
Markovic, T., Leon, M., Buffoni, D., & Punnekkat, S. (2022). Random forest based on federated learning for intrusion detection. In Maglogiannis, I., Iliadis, L., Macintyre, J., & Cortez, P. (Eds.), Artificial Intelligence Applications and Innovations (pp. 132–144). Cham: Springer International Publishing.
Maseer, Z. K., Yusof, R., Bahaman, N., Mostafa, S. A., & Foozy, C. F. M. (2021). Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset. IEEE Access, 9, 22351–22370.
Nettleton, D. (2014). Chapter 6 - Selection of variables and factor derivation. In Nettleton, D. (Ed.), Commercial Data Mining (pp. 79–104). Morgan Kaufmann.
Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konecny, J., Kumar, S., & McMahan, H. B. (2020). Adaptive federated optimization. arXiv preprint arXiv:2003.00295.
Riker, A., Mota, R., Rosário, D., Pereira, V., & Curado, M. (2022). Autonomic management of group communication for Internet of Things applications. International Journal of Communication Systems, 35(11), e5200.
Santo, Y., Abelém, A., Riker, A., & Tsiropoulou, E. E. (2025). Integrating data privacy and energy management in smart cities with partial sustainable IoT networks. IEEE Access.
Shen, T., Mishra, C. S., Sampson, J., Kandemir, M. T., & Narayanan, V. (2022). An efficient edge-cloud partitioning of random forests for distributed sensor networks. IEEE Embedded Systems Letters.
Silva, M., Riker, A., Torrado, J., Santos, J., & Curado, M. (2021). Extending energy neutral operation in Internet of Things. IEEE Internet of Things Journal, 9(10), 7510–7524.
Singhal, P., Pandey, S. R., & Popovski, P. (2024). Greedy Shapley client selection for communication-efficient federated learning. IEEE Networking Letters, 1–1.
Ullah, A., Anwar, S. M., Li, J., Nadeem, L., Mahmood, T., Rehman, A., & Saba, T. (2024). Smart cities: The role of Internet of Things and machine learning in realizing a data-centric smart environment. Complex & Intelligent Systems, 10(1), 1607–1637.
Yu, R., & Li, P. (2021). Toward resource-efficient federated learning in mobile edge computing. IEEE Network, 35(1), 148–155.
Publicado
19/05/2025
Como Citar
BARBOSA, Lucas; FERREIRA, Rodrigo; DALMAZO, Bruno L.; GONÇALVES, Glauco; LEAL, Adonis; RIKER, André.
Aprendizado Federado baseado em Múltiplas Árvores de Decisão para Computação de Borda IoT Cooperativa. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 9. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 169-182.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2025.9496.
