SCOPE: Segurança Quântica Sensível ao Contexto para Aprendizado Federado, Além do Superdimensionamento na Alocação de Chaves QKD
Resumo
A aplicação uniforme de Distribuição Quântica de Chaves (QKD) em Aprendizado Federado assume abundância de recursos criptográficos; contudo, implantações reais enfrentam taxas finitas de geração de chaves e contribuições heterogêneas dos clientes, tornando o superdimensionamento da segurança uma ineficiência sistêmica. Apresentamos o SCOPE, um framework escalável e sensível ao contexto que modela as chaves quânticas como um recurso restrito e aloca dinamicamente a proteção de acordo com o impacto dos clientes, a disponibilidade de chaves e critérios de equidade. Sua política permite proteção proporcional, superando regimes binários de segurança. Avaliado em um ambiente end-to-end que combina treinamento federado real com restrições de rede e QKD, o SCOPE reduz o consumo criptográfico em mais de 3×, praticamente elimina a escassez de chaves mesmo sob forte restrição e preserva a convergência e a robustez do modelo. Esses resultados demonstram que tratar a segurança quântica como uma variável de controle viabiliza um aprendizado federado quântico seguro, eficiente e escalável.Referências
Baruch, G., Baruch, M., and Goldberg, Y. (2019). A little is enough: Circumventing defenses for distributed learning. In Advances in Neural Information Processing Systems (NeurIPS).
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S. (2019). Analyzing federated learning through an adversarial lens. In Proceedings of the 36th International Conference on Machine Learning (ICML).
Bonawitz, K., Eichner, H., Grieskamp, W., et al. (2019). Towards federated learning at scale: System design. In Proceedings of the 2nd SysML Conference.
Freire, M., Mello, T. L., Sant’Anna, I., Maia, A., Moreira, R., Rivelino, R., and Peixoto, M. (2025). Rana: Uma abordagem híbrida para qkd bb84 com expansão e encapsulamento de chave. In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), pages 938–951. SBC.
Gisin, N., Ribordy, G., Tittel, W., and Zbinden, H. (2002). Quantum cryptography. Reviews of Modern Physics, 74(1):145–195.
Kairouz, P., McMahan, H. B., Avent, B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2):1–210.
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020). Federated optimization in heterogeneous networks. In Proceedings of Machine Learning and Systems (MLSys).
Lo, H.-K., Curty, M., and Qi, B. (2014). Secure quantum key distribution. Nature Photonics, 8(8):595–604.
Lyu, L., Yu, H., Ma, X., et al. (2020). Privacy and robustness in federated learning: Attacks and defenses. IEEE Transactions on Neural Networks and Learning Systems.
Maia, A., Freire, M., Mello, T., Rodrigues-Filho, R., Almeida, E., Prazeres, C., Figueiredo, G., and Peixoto, M. (2025). Q-edge: Leveraging quantum computing for enhanced software engineering in vehicular networks. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, pages 1457–1467.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
Peixoto, M. L. M. (2024). Quantum edge computing for data analysis in connected autonomous vehicles. In 2024 IEEE Symposium on Computers and Communications (ISCC), pages 1–6. IEEE.
Ponomareva, N., Hazimeh, H., Kurakin, A., Xu, Z., Denison, C., McMahan, H. B., Vassilvitskii, S., Chien, S., and Thakurta, A. G. (2023). How to dp-fy ml: A practical guide to machine learning with differential privacy. Journal of Artificial Intelligence Research, 77:1113–1201.
Puthal, D., Yeun, C. Y., Sharma, P. K., and Ding, Z. (2025). Privacy-preserving federated learning for the edge-intelligent 6g ecosystem. IEEE Communications Standards Magazine.
Sundaramurthy, A., Kathavarayan, R., Raju, K. K., Dayalan, S. P., Mohan, J., and Sivakolundu, V. P. (2025). Quantum-secured federated learning for privacy-preserving and adaptive attack detection in 6g iot. In Secure Communication for the 6G and the Internet of Things Networks, pages 253–276. CRC Press.
Truex, S., Baracaldo, N., Anwar, A., et al. (2019). A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security (AISec).
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S. (2019). Analyzing federated learning through an adversarial lens. In Proceedings of the 36th International Conference on Machine Learning (ICML).
Bonawitz, K., Eichner, H., Grieskamp, W., et al. (2019). Towards federated learning at scale: System design. In Proceedings of the 2nd SysML Conference.
Freire, M., Mello, T. L., Sant’Anna, I., Maia, A., Moreira, R., Rivelino, R., and Peixoto, M. (2025). Rana: Uma abordagem híbrida para qkd bb84 com expansão e encapsulamento de chave. In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), pages 938–951. SBC.
Gisin, N., Ribordy, G., Tittel, W., and Zbinden, H. (2002). Quantum cryptography. Reviews of Modern Physics, 74(1):145–195.
Kairouz, P., McMahan, H. B., Avent, B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2):1–210.
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020). Federated optimization in heterogeneous networks. In Proceedings of Machine Learning and Systems (MLSys).
Lo, H.-K., Curty, M., and Qi, B. (2014). Secure quantum key distribution. Nature Photonics, 8(8):595–604.
Lyu, L., Yu, H., Ma, X., et al. (2020). Privacy and robustness in federated learning: Attacks and defenses. IEEE Transactions on Neural Networks and Learning Systems.
Maia, A., Freire, M., Mello, T., Rodrigues-Filho, R., Almeida, E., Prazeres, C., Figueiredo, G., and Peixoto, M. (2025). Q-edge: Leveraging quantum computing for enhanced software engineering in vehicular networks. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, pages 1457–1467.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
Peixoto, M. L. M. (2024). Quantum edge computing for data analysis in connected autonomous vehicles. In 2024 IEEE Symposium on Computers and Communications (ISCC), pages 1–6. IEEE.
Ponomareva, N., Hazimeh, H., Kurakin, A., Xu, Z., Denison, C., McMahan, H. B., Vassilvitskii, S., Chien, S., and Thakurta, A. G. (2023). How to dp-fy ml: A practical guide to machine learning with differential privacy. Journal of Artificial Intelligence Research, 77:1113–1201.
Puthal, D., Yeun, C. Y., Sharma, P. K., and Ding, Z. (2025). Privacy-preserving federated learning for the edge-intelligent 6g ecosystem. IEEE Communications Standards Magazine.
Sundaramurthy, A., Kathavarayan, R., Raju, K. K., Dayalan, S. P., Mohan, J., and Sivakolundu, V. P. (2025). Quantum-secured federated learning for privacy-preserving and adaptive attack detection in 6g iot. In Secure Communication for the 6G and the Internet of Things Networks, pages 253–276. CRC Press.
Truex, S., Baracaldo, N., Anwar, A., et al. (2019). A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security (AISec).
Publicado
25/05/2026
Como Citar
MAIA, Adriano; SANT’ANNA, Isys; FREIRE, Marcus; MELLO, Thiago; TARDIOLE, Bruno; GUAZZELLI, Bruno; LEITE, Dionisio; PEIXOTO, Maycon.
SCOPE: Segurança Quântica Sensível ao Contexto para Aprendizado Federado, Além do Superdimensionamento na Alocação de Chaves QKD. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 44. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1261-1274.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2026.19925.
