Blockchain como fonte descentralizada de dados para LLMs
Resumo
O treinamento de modelos de linguagem de grande escala (LLMs) sobre dados provenientes de redes blockchain impõe um desafio fundamental de compatibilidade arquitetônica. A prática de treinamento centralizado entra em conflito direto com as garantias essenciais da tecnologia blockchain (e.g., imutabilidade, descentralização e transparência) pois sua operação depende da extração e centralização dos dados para agregação. Neste artigo, indicamos que a solução para este desafio reside em uma arquitetura de treinamento que preserve a descentralização. Propomos um modelo baseado em uma blockchain permissionada, que combina aprendizado federado descentralizado com a aplicação de privacidade diferencial, para oferecer garantias contra a inferência de informações sensíveis por terceiros.Referências
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Gupta, S., Huang, Y., Zhong, Z., Gao, T., Li, K., and Chen, D. (2022). Recovering Private Text in Federated Learning of Language Models.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685 [cs].
Jiao, Q. and Zhang, S. (2021). A Brief Survey of Word Embedding and Its Recent Development. In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pages 1697–1701, Chongqing, China. IEEE.
Kitchenham, B. (2004). Procedures for Performing Systematic Reviews.
Kozgunov, N. V., Khalashi, M. H., Oliseenko, V. D., and Tulupyeva, T. V. (2024). Lingua-Chain: a Peer-to-peer Dynamic Decentralized Large Language Model with Coin-based Incentives. In 2024 XXVII International Conference on Soft Computing and Measurements (SCM), pages 178–181, Saint Petersburg, Russian Federation. IEEE.
Luo, H., Luo, J., and Vasilakos, A. V. (2024). BC4LLM: A perspective of trusted artificial intelligence when blockchain meets large language models. Neurocomputing, 599:128089.
McMahan, H. B., Moore, E., Ramage, D., and Hampson, S. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data.
Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., and Gao, J. (2024). Large Language Models: A Survey. arXiv:2402.06196 [cs].
Morais, M., Costa, J., Gonzalez, L., Souza, A., and Villas, L. (2024). Mecanismo para mitigar ataques de envenenamento de modelo no aprendizado federado. In Anais do VIII Workshop de Computação Urbana, pages 224–237, Porto Alegre, RS, Brasil. SBC.
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Su, H., Xiang, C., and Ramesh, B. (2024). Towards Confidential Chatbot Conversations: A Decentralised Federated Learning Framework. The Journal of The British Blockchain Association, 7(1):1–8.
Subrmanian, K., Thangarasu, G., Yanyan, Z., and Kannan, K. N. (2024). Deep Learning Based Algorithm for Efficient Information Retrieval in Blockchain Transactions. In 2024 IEEE 6th Symposium on Computers & Informatics (ISCI), pages 264–269, Kuala Lumpur, Malaysia. IEEE.
Sunny, F. A., Hajek, P., Munk, M., Abedin, M. Z., Satu, M. S., Efat, M. I. A., and Islam, M. J. (2022). A Systematic Review of Blockchain Applications. IEEE Access, 10:59155–59177.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv:2302.13971 [cs].
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is All you Need. Advances in neural information processing systems, 30.
Wang, H., Cai, Y., Tao, Y., Wang, L., Li, Y., and Zhou, L. (2025). B2DFL: Bringing butterfly to decentralized federated learning assisted with blockchain. Journal of Parallel and Distributed Computing, 195:104978.
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., and Liu, Y. (2019). A survey on the scalability of blockchain systems. IEEE network, 33(5):166–173.
Xu, X., Weber, I., and Staples, M. (2019). Architecture for Blockchain Applications. Springer International Publishing, Cham.
Zuo, X., Wang, M., Zhu, T., Zhang, L., Ye, D., Yu, S., and Zhou, W. (2024). Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning. arXiv:2406.04076 [cs].
Ampel, B., Patton, M., and Chen, H. (2019). Performance modeling of hyperledger saw-tooth blockchain. In 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), pages 59–61. IEEE.
Balija, S. B., Nanda, A., and Sahoo, D. (2024). Building Communication Efficient Asynchronous Peer-to-Peer Federated LLMs with Blockchain. Proceedings of the AAAI Symposium Series, 3(1):288–292.
Batool, Z., Zhang, K., Zhu, Z., Aravamuthan, S., and Aivodji, U. (2022). Block-FeST: A Blockchain-Based Federated Anomaly Detection framework with computation offloading using Transformers. In 2022 IEEE 1st Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), pages 1–6, Irvine, CA, USA. IEEE.
Buterin, V. (2014). A Next Generation Smart Contract & Decentralized Application Platform.
Cai, Z., Chen, J., Fan, Y., Zheng, Z., and Li, K. (2024). Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions. arXiv:2403.00873 [cs].
Gao, Y., Song, Z., and Yin, J. (2023). GradientCoin: A Peer-to-Peer Decentralized Large Language Models. arXiv:2308.10502 [cs].
Gupta, S., Huang, Y., Zhong, Z., Gao, T., Li, K., and Chen, D. (2022). Recovering Private Text in Federated Learning of Language Models.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685 [cs].
Jiao, Q. and Zhang, S. (2021). A Brief Survey of Word Embedding and Its Recent Development. In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pages 1697–1701, Chongqing, China. IEEE.
Kitchenham, B. (2004). Procedures for Performing Systematic Reviews.
Kozgunov, N. V., Khalashi, M. H., Oliseenko, V. D., and Tulupyeva, T. V. (2024). Lingua-Chain: a Peer-to-peer Dynamic Decentralized Large Language Model with Coin-based Incentives. In 2024 XXVII International Conference on Soft Computing and Measurements (SCM), pages 178–181, Saint Petersburg, Russian Federation. IEEE.
Luo, H., Luo, J., and Vasilakos, A. V. (2024). BC4LLM: A perspective of trusted artificial intelligence when blockchain meets large language models. Neurocomputing, 599:128089.
McMahan, H. B., Moore, E., Ramage, D., and Hampson, S. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data.
Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., and Gao, J. (2024). Large Language Models: A Survey. arXiv:2402.06196 [cs].
Morais, M., Costa, J., Gonzalez, L., Souza, A., and Villas, L. (2024). Mecanismo para mitigar ataques de envenenamento de modelo no aprendizado federado. In Anais do VIII Workshop de Computação Urbana, pages 224–237, Porto Alegre, RS, Brasil. SBC.
Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System.
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., Avila, R., Babuschkin, I., Balaji, S., Balcom, V., Baltescu, P., Bao, H., Bavarian, M., Belgum, J., Bello, I., Berdine, J., Bernadett-Shapiro, G., Berner, C., Bogdonoff, L., Boiko, O., Boyd, M., Brakman, A.-L., Brockman, G., Brooks, T., Brundage, M., Button, K., Cai, T., Campbell, R., Cann, A., Carey, B., Carlson, C., Carmichael, R., Chan, B., Chang, C., Chantzis, F., Chen, D., Chen, S., Chen, R., Chen, J., Chen, M., Chess, B., Cho, C., Chu, C., Chung, H. W., Cummings, D., Currier, J., Dai, Y., Decareaux, C., Degry, T., Deutsch, N., Deville, D., Dhar, A., Dohan, D., Dowling, S., Dunning, S., Ecoffet, A., Eleti, A., Eloundou, T., Farhi, D., Fedus, L., Felix, N., Fishman, S. P., Forte, J., Fulford, I., Gao, L., Georges, E., Gibson, C., Goel, V., Gogineni, T., Goh, G., Gontijo-Lopes, R., Gordon, J., Grafstein, M., Gray, S., Greene, R., Gross, J., Gu, S. S., Guo, Y., Hallacy, C., Han, J., Harris, J., He, Y., Heaton, M., Heidecke, J., Hesse, C., Hickey, A., Hickey, W., Hoeschele, P., Houghton, B., Hsu, K., Hu, S., Hu, X., Huizinga, J., Jain, S., Jain, S., Jang, J., Jiang, A., Jiang, R., Jin, H., Jin, D., Jomoto, S., Jonn, B., Jun, H., Kaftan, T., Kaiser, L., Kamali, A., Kanitscheider, I., Keskar, N. S., Khan, T., Kilpatrick, L., Kim, J. W., Kim, C., Kim, Y., Kirchner, J. H., Kiros, J., Knight, M., Kokotajlo, D., Kondraciuk, L., Kondrich, A., Konstantinidis, A., Kosic, K., Krueger, G., Kuo, V., Lampe, M., Lan, I., Lee, T., Leike, J., Leung, J., Levy, D., Li, C. M., Lim, R., Lin, M., Lin, S., Litwin, M., Lopez, T., Lowe, R., Lue, P., Makanju, A., Malfacini, K., Manning, S., Markov, T., Markovski, Y., Martin, B., Mayer, K., Mayne, A., McGrew, B., McKinney, S. M., McLeavey, C., McMillan, P., McNeil, J., Medina, D., Mehta, A., Menick, J., Metz, L., Mishchenko, A., Mishkin, P., Monaco, V., Morikawa, E., Mossing, D., Mu, T., Murati, M., Murk, O., Mély, D., Nair, A., Nakano, R., Nayak, R., Neelakantan, A., Ngo, R., Noh, H., Ouyang, L., O’Keefe, C., Pachocki, J., Paino, A., Palermo, J., Pantuliano, A., Parascandolo, G., Parish, J., Parparita, E., Passos, A., Pavlov, M., Peng, A., Perelman, A., Peres, F. d. A. B., Petrov, M., Pinto, H. P. d. O., Michael, Pokorny, Pokrass, M., Pong, V. H., Powell, T., Power, A., Power, B., Proehl, E., Puri, R., Radford, A., Rae, J., Ramesh, A., Raymond, C., Real, F., Rimbach, K., Ross, C., Rotsted, B., Roussez, H., Ryder, N., Saltarelli, M., Sanders, T., Santurkar, S., Sastry, G., Schmidt, H., Schnurr, D., Schulman, J., Selsam, D., Sheppard, K., Sherbakov, T., Shieh, J., Shoker, S., Shyam, P., Sidor, S., Sigler, E., Simens, M., Sitkin, J., Slama, K., Sohl, I., Sokolowsky, B., Song, Y., Staudacher, N., Such, F. P., Summers, N., Sutskever, I., Tang, J., Tezak, N., Thompson, M. B., Tillet, P., Tootoonchian, A., Tseng, E., Tuggle, P., Turley, N., Tworek, J., Uribe, J. F. C., Vallone, A., Vijayvergiya, A., Voss, C., Wainwright, C., Wang, J. J., Wang, A., Wang, B., Ward, J., Wei, J., Weinmann, C. J., Welihinda, A., Welinder, P., Weng, J., Weng, L., Wiethoff, M., Willner, D., Winter, C., Wolrich, S., Wong, H., Workman, L., Wu, S., Wu, J., Wu, M., Xiao, K., Xu, T., Yoo, S., Yu, K., Yuan, Q., Zaremba, W., Zellers, R., Zhang, C., Zhang, M., Zhao, S., Zheng, T., Zhuang, J., Zhuk, W., and Zoph, B. (2024). GPT-4 Technical Report. arXiv:2303.08774 [cs].
Su, H., Xiang, C., and Ramesh, B. (2024). Towards Confidential Chatbot Conversations: A Decentralised Federated Learning Framework. The Journal of The British Blockchain Association, 7(1):1–8.
Subrmanian, K., Thangarasu, G., Yanyan, Z., and Kannan, K. N. (2024). Deep Learning Based Algorithm for Efficient Information Retrieval in Blockchain Transactions. In 2024 IEEE 6th Symposium on Computers & Informatics (ISCI), pages 264–269, Kuala Lumpur, Malaysia. IEEE.
Sunny, F. A., Hajek, P., Munk, M., Abedin, M. Z., Satu, M. S., Efat, M. I. A., and Islam, M. J. (2022). A Systematic Review of Blockchain Applications. IEEE Access, 10:59155–59177.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv:2302.13971 [cs].
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). Attention is All you Need. Advances in neural information processing systems, 30.
Wang, H., Cai, Y., Tao, Y., Wang, L., Li, Y., and Zhou, L. (2025). B2DFL: Bringing butterfly to decentralized federated learning assisted with blockchain. Journal of Parallel and Distributed Computing, 195:104978.
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., and Liu, Y. (2019). A survey on the scalability of blockchain systems. IEEE network, 33(5):166–173.
Xu, X., Weber, I., and Staples, M. (2019). Architecture for Blockchain Applications. Springer International Publishing, Cham.
Zuo, X., Wang, M., Zhu, T., Zhang, L., Ye, D., Yu, S., and Zhou, W. (2024). Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning. arXiv:2406.04076 [cs].
Publicado
27/10/2025
Como Citar
DELAZZARI, Ana Regina; PALMA, Lucas Machado da; MARTINA, Jean Everson.
Blockchain como fonte descentralizada de dados para LLMs. In: LABLOCK ARTIGOS CURTOS - LATIN-AMERICAN SYMPOSIUM ON DEPENDABLE AND SECURE COMPUTING (LADC), 14. , 2025, Valparaíso/Chile.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 29-38.
DOI: https://doi.org/10.5753/ladc_estendido.2025.16883.
