Divergência de Peso em Aprendizado Federado: Impacto no Modelo Global Considerando Dados Non-IID
Resumo
Este estudo investiga como a divergência de peso pode servir como um indicador confiável da contribuição de um dispositivo para o modelo global em sistemas de Aprendizado Federado (AF), permitindo a omissão da transmissão do modelo quando a contribuição é mínima. Este estudo é dividido em duas partes principais. Primeiro, foi realizada uma análise do método de agendamento de dispositivos apresentado no artigo “Federated Learning Over Wireless IoT Networks”. Em seguida, este estudo propõe um algoritmo que emprega regularização L2 para o agendamento de dispositivos IoT ultrarrestritos, considerando a heterogeneidade dos dados (dados no-IDD), com o objetivo de reduzir o consumo de energia minimizando o número de transmissões de modelos desatualizados para o servidor central. Os resultados indicam que a abordagem proposta pode otimizar a eficiência energética e a comunicação em redes IoT em comparação com os métodos tradicionais.
Palavras-chave:
Aprendizado Federado, Non-IDD, Divergência de Peso
Referências
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J. Henkel, S. Pagani, H. Amrouch, L. Bauer, and F. Samie, “Ultralow power and dependability for iot devices (invited paper for iot technologies),” in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. IEEE, 2017, pp. 954–959.
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H. N. C. Neto, D. M. F. Mattos, and N. C. Fernandes, “Privacidade do usuário em aprendizado colaborativo: Federated learning, da teoria à prática,” in Minicursos do Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg), 2020, vol. 20, pp. 142–195.
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Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” arXiv preprint arXiv:1806.00582, 2018. [Online]. Available: [link]
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R. Pereira and N. Santos, “Indústria 5.0: reflexões sobre uma nova abordagem paradigmática para a indústria,” ANPAD. EnANPAD, pp. 2177–2576, 2022.
A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015. [Online]. Available: [link]
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J. Ni, X. Lin, and X. S. Shen, “Toward edge-assisted internet of things: From security and efficiency perspectives,” IEEE Network, vol. 33, no. 2, pp. 50–57, 2019.
H. Habibzadeh, T. Soyata, B. Kantarci, A. Boukerche, and C. S. Kaptan, “Sensing, communication and security planes: A new challenge for a smart city system design,” Computer Networks, vol. 144, pp. 163–200, 2018. [Online]. DOI: 10.1016/j.comnet.2018.08.001
P. Asghari, A. M. Rahmani, and H. H. S. Javadi, “Internet of things applications: A systematic review,” Computer Networks, vol. 148, pp. 241–261, Jan. 2019. [Online]. DOI: 10.1016/j.comnet.2018.12.008
H. Elazhary, “Internet of things (iot), mobile cloud, cloudlet, mobile iot, iot cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions,” Journal of Network and Computer Applications, vol. 128, pp. 105–140, 2019. [Online]. DOI: 10.1016/j.jnca.2018.10.021
M. G. Samaila, M. Neto, D. A. B. Fernandes, M. M. Freire, and P. R. M. Inácio, “Challenges of securing internet of things devices: A survey,” Security and Privacy, vol. 1, no. 2, p. e20, mar 2018. [Online]. Available: [link]
K. Sha, W. Wei, T. A. Yang, Z. Wang, and W. Shi, “On security challenges and open issues in internet of things,” Future Generation Computer Systems, vol. 83, pp. 326–337, 2018. [Online]. DOI: 10.1016/j.future.2018.01.059
F. Montori, L. Bedogni, M. Di Felice, and L. Bononi, “Machineto-machine wireless communication technologies for the internet of things: Taxonomy, comparison and open issues,” Pervasive and Mobile Computing, vol. 52, pp. 56–81, 2018. [Online]. DOI: 10.1016/j.pmcj.2018.08.002
A. Čolaković and M. Hadžialić, “Internet of things (iot): A review of enabling technologies, challenges, and open research issues,” Computer Networks, vol. 144, pp. 17–39, 2018. [Online]. Available: [link]
Z. Shelby and C. Bormann, 6LoWPAN: The Wireless Embedded Internet. Hoboken, NJ: Wiley, 2011.
T. Yigitcanlar, M. Kamruzzaman, M. Foth, J. Sabatini-Marques, E. da Costa, and G. Ioppolo, “Can cities become smart without being sustainable? a systematic review of the literature,” Sustainable Cities and Society, vol. 45, pp. 348–365, feb 2019. [Online]. DOI: 10.1016/j.scs.2018.11.033
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A. C. Riekstin and [others], “A survey on metrics and measurement tools for sustainable distributed cloud networks,” IEEE Communications Surveys and Tutorials, vol. 20, no. 2, pp. 1244–1270, 2018. [Online]. Available: [link]
F. Jalali, K. Hinton, R. Ayre, T. Alpcan, and R. S. Tucker, “Fog computing may help to save energy in cloud computing,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 5, pp. 1728–1739, 2016.
B. Martinez, M. Montón, X. Vilajosana, and J. D. Prades, “The power of models: Modeling power consumption for iot devices,” IEEE Sensors Journal, vol. 15, no. 10, pp. 5777–5789, 2015.
E. Ahvar, A.-C. Orgerie, and A. Lebre, “Estimating energy consumption of cloud, fog, and edge computing infrastructures,” IEEE Transactions on Sustainable Computing, vol. 7, no. 2, pp. 277–288, apr 2022. [Online]. Available: [link]
K. Hsieh, A. Phanishayee, O. Mutlu, and P. B. Gibbons, “The non-iid data quagmire of decentralized machine learning,” arXiv preprint arXiv:1909.0089, 2019. [Online]. Available: [link]
F. Sattler, S. Wiedemann, K. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 9, pp. 3400–3413, 2020. [Online]. DOI: 10.1109/TNNLS.2019.2944481
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-iid data,” arXiv preprint arXiv:1907.0289, 2019. [Online]. Available: [link]
F. Sattler, K. Müller, and W. Samek, “Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints,” IEEE Transactions on Neural Networks and Learning Systems, 2020.
B. Luo, Y. Liu, Y. Ji, Y. Cheng, and H. Wang, “Tackling system and statistical heterogeneity for federated learning with adaptive client sampling,” arXiv preprint arXiv:2112.11256, 2021.
F. Lai, X. Zhu, H. V. Madhyastha, and M. Chowdhury, “Oort: Efficient federated learning via guided participant selection,” in USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2021, pp. 19–35.
H. Wang, Z. Kaplan, D. Niu, and B. Li, “Optimizing federated learning on non-iid data with reinforcement learning,” in IEEE INFOCOM, 2020, pp. 1698–1707.
H. N. Neto, M. R. Oliveira, R. P. Silva, and D. J. Ferreira, “Fedsa: Arrefecimento simulado federado para a aceleração da detecção de intrusão em ambientes colaborativos,” in Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2021, pp. 280–293.
D. K. Dennis, T. Li, and V. Smith, “Heterogeneity for the win: One-shot federated clustering,” arXiv preprint arXiv:2103.00697, 2021.
A. Ghosh, J. Chung, D. Yin, and K. Ramchandran, “An efficient framework for clustered federated learning,” arXiv preprint arXiv:2006.04088, 2020.
F. Sattler, K.-R. Müller, and W. Samek, “Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints,” IEEE Transactions on Neural Networks and Learning Systems, 2020.
X. Ouyang, J. Li, Z. Wang, Y. Gao, Y. Yu, and X. Qiu, “Clusterfl: A similarity-aware federated learning system for human activity recognition,” in Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys), 2021, pp. 54–66.
G. van Rossum and F. L. Drake, Python reference manual. Virginia, USA: PythonLabs, 1995.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, ..., and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from [link]. [Online]. Available: [link]
S. Haykin, Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ, USA: Prentice Hall PTR, 1994.
L. B. Almeida, “Multilayer perceptrons,” in Handbook of Neural Computation. Bristol, UK: IOP Publishing Ltd and Oxford University Press, 1997.
Y. LeCun, C. Cortes, and C. J. C. Burges, “MNIST handwritten digit database,” ATT Labs [Online], vol. 2, 2010.
Y. LeCun and C. Cortes, “Mnist handwritten digit database,” ATT Labs [Online], vol. 2, 2010. [Online]. Available: [link]
L. Wasserman, All of Statistics: A Concise Course in Statistical Inference, ser. Springer Texts in Statistics. Springer, 2004. [Online]. Available: [link]
L. Bottou, “Large-scale machine learning with stochastic gradient descent,” Proc. of COMPSTAT, 01 2010.
N. Draper and H. Smith, Applied Regression Analysis, ser. Wiley Series in Probability and Statistics. Wiley, 1998. [Online]. Available: [link]
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, pp. 818–833.
J. Frost, Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models. Statistics by Jim Publishing, 2020. [Online]. Available: [link]
H. N. C. Neto, D. M. F. Mattos, and N. C. Fernandes, “Privacidade do usuário em aprendizado colaborativo: Federated learning, da teoria à prática,” in Simpósio Brasileiro em Segurança da Informação e de Sistemas Computacionais (SBSeg). Brasil: Sociedade Brasileira de Computação (SBC), 2020, capítulo realizado com recursos do CNPq, CAPES, RNP, FAPERJ e FAPESP (2018/23062-5).
H. Zhu, J. Xu, S. Liu, and Y. Jin, “Federated learning on non-iid data: A survey,” Neurocomputing, vol. 465, 09 2021.
H. Chen, S. Huang, D. Zhang, M. Xiao, M. Skoglund, and H. V. Poor, “Federated learning over wireless iot networks with optimized communication and resources,” IEEE Internet of Things Journal, vol. 9, no. 17, pp. 16 592–16 605, 2022.
J. Henkel, S. Pagani, H. Amrouch, L. Bauer, and F. Samie, “Ultralow power and dependability for iot devices (invited paper for iot technologies),” in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. IEEE, 2017, pp. 954–959.
GitHub, “Github: Where the world builds software,” [link], 2025, acessado em: jun. 26, 2025.
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), ser. Proceedings of Machine Learning Research, A. Singh and X. Zhu, Eds., vol. 54. Fort Lauderdale, FL, USA: PMLR, 2017, pp. 1273–1282.
H. N. C. Neto, D. M. F. Mattos, and N. C. Fernandes, “Privacidade do usuário em aprendizado colaborativo: Federated learning, da teoria à prática,” in Minicursos do Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg), 2020, vol. 20, pp. 142–195.
W. Y. B. Lim, N. C. Luong, D. T. Hoang, Y. Liang, Q. Yang, D. Niyato, and C. Miao, “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031–2063, 2020.
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” arXiv preprint arXiv:1806.00582, 2018. [Online]. Available: [link]
N.-D. Tran, W. Bao, W. Saad, D. T. Hoang, and D. Niyato, “Federated learning over wireless networks: Optimization model design and analysis,” in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. IEEE, 2019, pp. 1387–1395.
R. Pereira and N. Santos, “Indústria 5.0: reflexões sobre uma nova abordagem paradigmática para a indústria,” ANPAD. EnANPAD, pp. 2177–2576, 2022.
A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015. [Online]. Available: [link]
D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of things: Vision, applications and research challenges,” Ad Hoc Networks, vol. 10, no. 7, pp. 1497–1516, Sep. 2012. [Online]. DOI: 10.1016/j.adhoc.2012.02.016
J. Ni, X. Lin, and X. S. Shen, “Toward edge-assisted internet of things: From security and efficiency perspectives,” IEEE Network, vol. 33, no. 2, pp. 50–57, 2019.
H. Habibzadeh, T. Soyata, B. Kantarci, A. Boukerche, and C. S. Kaptan, “Sensing, communication and security planes: A new challenge for a smart city system design,” Computer Networks, vol. 144, pp. 163–200, 2018. [Online]. DOI: 10.1016/j.comnet.2018.08.001
P. Asghari, A. M. Rahmani, and H. H. S. Javadi, “Internet of things applications: A systematic review,” Computer Networks, vol. 148, pp. 241–261, Jan. 2019. [Online]. DOI: 10.1016/j.comnet.2018.12.008
H. Elazhary, “Internet of things (iot), mobile cloud, cloudlet, mobile iot, iot cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions,” Journal of Network and Computer Applications, vol. 128, pp. 105–140, 2019. [Online]. DOI: 10.1016/j.jnca.2018.10.021
M. G. Samaila, M. Neto, D. A. B. Fernandes, M. M. Freire, and P. R. M. Inácio, “Challenges of securing internet of things devices: A survey,” Security and Privacy, vol. 1, no. 2, p. e20, mar 2018. [Online]. Available: [link]
K. Sha, W. Wei, T. A. Yang, Z. Wang, and W. Shi, “On security challenges and open issues in internet of things,” Future Generation Computer Systems, vol. 83, pp. 326–337, 2018. [Online]. DOI: 10.1016/j.future.2018.01.059
F. Montori, L. Bedogni, M. Di Felice, and L. Bononi, “Machineto-machine wireless communication technologies for the internet of things: Taxonomy, comparison and open issues,” Pervasive and Mobile Computing, vol. 52, pp. 56–81, 2018. [Online]. DOI: 10.1016/j.pmcj.2018.08.002
A. Čolaković and M. Hadžialić, “Internet of things (iot): A review of enabling technologies, challenges, and open research issues,” Computer Networks, vol. 144, pp. 17–39, 2018. [Online]. Available: [link]
Z. Shelby and C. Bormann, 6LoWPAN: The Wireless Embedded Internet. Hoboken, NJ: Wiley, 2011.
T. Yigitcanlar, M. Kamruzzaman, M. Foth, J. Sabatini-Marques, E. da Costa, and G. Ioppolo, “Can cities become smart without being sustainable? a systematic review of the literature,” Sustainable Cities and Society, vol. 45, pp. 348–365, feb 2019. [Online]. DOI: 10.1016/j.scs.2018.11.033
B. N. Silva, M. Khan, and K. Han, “Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities,” Sustainable Cities and Society, vol. 38, pp. 697–713, 2018. [Online]. DOI: 10.1016/j.scs.2018.01.053
A. C. Riekstin and [others], “A survey on metrics and measurement tools for sustainable distributed cloud networks,” IEEE Communications Surveys and Tutorials, vol. 20, no. 2, pp. 1244–1270, 2018. [Online]. Available: [link]
F. Jalali, K. Hinton, R. Ayre, T. Alpcan, and R. S. Tucker, “Fog computing may help to save energy in cloud computing,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 5, pp. 1728–1739, 2016.
B. Martinez, M. Montón, X. Vilajosana, and J. D. Prades, “The power of models: Modeling power consumption for iot devices,” IEEE Sensors Journal, vol. 15, no. 10, pp. 5777–5789, 2015.
E. Ahvar, A.-C. Orgerie, and A. Lebre, “Estimating energy consumption of cloud, fog, and edge computing infrastructures,” IEEE Transactions on Sustainable Computing, vol. 7, no. 2, pp. 277–288, apr 2022. [Online]. Available: [link]
K. Hsieh, A. Phanishayee, O. Mutlu, and P. B. Gibbons, “The non-iid data quagmire of decentralized machine learning,” arXiv preprint arXiv:1909.0089, 2019. [Online]. Available: [link]
F. Sattler, S. Wiedemann, K. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 9, pp. 3400–3413, 2020. [Online]. DOI: 10.1109/TNNLS.2019.2944481
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-iid data,” arXiv preprint arXiv:1907.0289, 2019. [Online]. Available: [link]
F. Sattler, K. Müller, and W. Samek, “Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints,” IEEE Transactions on Neural Networks and Learning Systems, 2020.
B. Luo, Y. Liu, Y. Ji, Y. Cheng, and H. Wang, “Tackling system and statistical heterogeneity for federated learning with adaptive client sampling,” arXiv preprint arXiv:2112.11256, 2021.
F. Lai, X. Zhu, H. V. Madhyastha, and M. Chowdhury, “Oort: Efficient federated learning via guided participant selection,” in USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2021, pp. 19–35.
H. Wang, Z. Kaplan, D. Niu, and B. Li, “Optimizing federated learning on non-iid data with reinforcement learning,” in IEEE INFOCOM, 2020, pp. 1698–1707.
H. N. Neto, M. R. Oliveira, R. P. Silva, and D. J. Ferreira, “Fedsa: Arrefecimento simulado federado para a aceleração da detecção de intrusão em ambientes colaborativos,” in Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2021, pp. 280–293.
D. K. Dennis, T. Li, and V. Smith, “Heterogeneity for the win: One-shot federated clustering,” arXiv preprint arXiv:2103.00697, 2021.
A. Ghosh, J. Chung, D. Yin, and K. Ramchandran, “An efficient framework for clustered federated learning,” arXiv preprint arXiv:2006.04088, 2020.
F. Sattler, K.-R. Müller, and W. Samek, “Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints,” IEEE Transactions on Neural Networks and Learning Systems, 2020.
X. Ouyang, J. Li, Z. Wang, Y. Gao, Y. Yu, and X. Qiu, “Clusterfl: A similarity-aware federated learning system for human activity recognition,” in Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys), 2021, pp. 54–66.
G. van Rossum and F. L. Drake, Python reference manual. Virginia, USA: PythonLabs, 1995.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, ..., and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from [link]. [Online]. Available: [link]
S. Haykin, Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ, USA: Prentice Hall PTR, 1994.
L. B. Almeida, “Multilayer perceptrons,” in Handbook of Neural Computation. Bristol, UK: IOP Publishing Ltd and Oxford University Press, 1997.
Y. LeCun, C. Cortes, and C. J. C. Burges, “MNIST handwritten digit database,” ATT Labs [Online], vol. 2, 2010.
Y. LeCun and C. Cortes, “Mnist handwritten digit database,” ATT Labs [Online], vol. 2, 2010. [Online]. Available: [link]
L. Wasserman, All of Statistics: A Concise Course in Statistical Inference, ser. Springer Texts in Statistics. Springer, 2004. [Online]. Available: [link]
L. Bottou, “Large-scale machine learning with stochastic gradient descent,” Proc. of COMPSTAT, 01 2010.
N. Draper and H. Smith, Applied Regression Analysis, ser. Wiley Series in Probability and Statistics. Wiley, 1998. [Online]. Available: [link]
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, pp. 818–833.
J. Frost, Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models. Statistics by Jim Publishing, 2020. [Online]. Available: [link]
Publicado
22/10/2025
Como Citar
OLIVEIRA, Deivision; SOUZA, Mateus; CHAGAS, Ana Luísa de Bastos; LIMA, Maurício Rodrigues; OLIVEIRA, Antonio; OLIVEIRA, Renan Rodrigues de; FERREIRA, Deller; DIAS, Elisângela Silva.
Divergência de Peso em Aprendizado Federado: Impacto no Modelo Global Considerando Dados Non-IID. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 22. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 1-9.
DOI: https://doi.org/10.5753/latinoware.2025.14207.
