Aprendizado Federado Incremental e Sensível ao Risco para Modelos de Ranqueamento em Cenários com Distribuições Heterogêneas de Dados
Resumo
Esta dissertação apresenta o FedRisk, uma abordagem inédita de Federated Learning to Rank (FLTR) projetada para enfrentar um dos desafios mais críticos dos Sistemas de Informação distribuídos: a agregação de modelos sob heterogeneidade e distribuições não independentes e não identicamente distribuídas (não-IID). A proposta integra um mecanismo de agregação sensível ao risco — capaz de ponderar as atualizações dos clientes de acordo com a variabilidade do erro de predição — a uma estratégia de estabilização baseada na incorporação de parâmetros globais históricos, promovendo maior robustez e estabilidade no processo de convergência federada. Experimentos extensivos no benchmark MSLR-WEB10K demonstram que o FedRisk supera abordagens federadas consolidadas, como o FedProx, alcançando ganho de 15,6% em nDCG@5 e desempenho equivalente ao treinamento centralizado em nDCG@10, além de reduzir substancialmente a variância ao longo das rodadas de comunicação. Para além dos ganhos empíricos, o trabalho apresenta uma formulação principiada de agregação sensível ao risco para FLTR, uma análise sistemática dos efeitos da heterogeneidade no ranqueamento federado e uma validação experimental abrangente em cenários distribuídos realistas. Ao integrar Recuperação de Informação, Aprendizado de Máquina Distribuído e princípios de privacidade e governança de dados, esta dissertação contribui para o avanço de Sistemas de Informação escaláveis, inteligentes e alinhados a requisitos regulatórios contemporâneos. Sua relevância científica foi reconhecida com o prêmio de Honra ao Mérito de Melhor Artigo Completo no SBBD 2025.Referências
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Brownlee, J. (2018). Statistical Methods for Machine Learning. Machine Learning Mastery.
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Dincer, B., Zhu, Y., Craswell, N., and Zhang, M. (2016). Risk-sensitive evaluation and learning to rank using multiple baselines. In ACM SIGIR, pages 483–492.
Divi, S., Lin, Y.-S., Farrukh, H., and Celik, Z. B. (2021). New metrics to evaluate the performance and fairness of personalized federated learning.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. SAGE Publications, 4 edition.
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McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2023). Communication-efficient learning of deep networks from decentralized data.
Mukut, S., Kakoli, G., and Jyotika, B. (2012). Federated search: An information retrieval strategy for scholarly literature.
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Qin, T. and Liu, T. (2013). Introducing LETOR 4.0 datasets. CoRR, abs/1306.2597.
Rodrigues, P. H., Danielde Sousa, França, C., Rabbi, G., Rosa, T., and Gonçalves, M. A. (2025). Risk-sensitive optimization of neural deep learning ranking models with applications in ad-hoc retrieval and recommender systems. IP&M, 62(4):104126.
Rodrigues, P. H. S., Xavier Sousa, D., Couto Rosa, T., and Gonçalves, M. A. (2022). Risk-sensitive deep neural learning to rank. In ACM SIGIR, page 803–813.
Spiegelhalter, D. (2024). The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck. Pelican Books.
Voorhees, E. M. (1999). The trec-8 question answering track report. In TREC-8. National Institute of Standards and Technology (NIST).
Wang, H., Xu, H., Li, Y., Xu, Y., Li, R., and Zhang, T. (2024). FedCDA: Federated learning with cross-rounds divergence-aware aggregation. In ICLR.
Wang, J. and Liu, M. (2020). Tackling the objective inconsistency problem in heterogeneous federated optimization. In NeurIPS.
Wang, S. (2024). Effective and secure federated online learning to rank. arXiv preprint arXiv:2412.19069.
Wang, S. and Zuccon, G. (2022). Is non-iid data a threat in federated online learning to rank? In ACM SIGIR Conference, SIGIR ’22, page 2801–2813.
Wang, Y., Li, T.-Y., Wang, D., and Zhu, M. (2013). A theoretical analysis of ndcg type ranking measures. Journal of Machine Learning Research, 14:25–54.
Yu, T., Bagdasaryan, E., and Shmatikov, V. (2022). Salvaging federated learning by local adaptation.
Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, T. N., and Khazaeni, Y. (2019). Bayesian nonparametric federated learning of neural networks.
Zhao, S. et al. (2024). Federated risk-aware learning with central sensitivity estimation. arXiv preprint arXiv:2502.17694.
Zhu, H., Jin, B., Li, H., and Liang, X. (2021). Federated learning on non-iid data: A survey. Neurocomputing, 465:371–390.
Ai, Q., Bi, K., Guo, J., and Croft, W. B. (2018). Learning a deep listwise context model for ranking refinement. In ACM SIGIR Conference. ACM.
Araujo, R. M. d., Maciel, R. S. P., and Boscarioli, C. (2017). I GranDSI-BR: Grandes Desafios de Pesquisa em Sistemas de Informação no Brasil (2016–2026). Technical report, Comissão Especial de Sistemas de Informação (CE-SI), Sociedade Brasileira de Computação (SBC). Relatório Técnico.
Berrar, D. (2018). Cross-Validation.
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Kwing, H. L., Parcollet, T., Gusmão, P. P. d., and Lane, N. D. (2020). Flower: A friendly federated learning research framework. arXiv preprint arXiv:2007.14390.
Brownlee, J. (2018). Statistical Methods for Machine Learning. Machine Learning Mastery.
Chen, S. et al. (2021). Risk-aware federated learning in crowdsensing systems. arXiv preprint arXiv:2101.01266.
Dincer, B., Zhu, Y., Craswell, N., and Zhang, M. (2016). Risk-sensitive evaluation and learning to rank using multiple baselines. In ACM SIGIR, pages 483–492.
Divi, S., Lin, Y.-S., Farrukh, H., and Celik, Z. B. (2021). New metrics to evaluate the performance and fairness of personalized federated learning.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. SAGE Publications, 4 edition.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
Hu, C. (2024). Improving federated learning accuracy with the incremental averaging method: A comparative analysis of model aggregation techniques. In Applied and Computational Engineering, pages 150–157. EWA Publishing.
Järvelin, K. and Kekäläinen, J. (2002). Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS), 20(4):422–446.
Jiménez-Gutiérrez, D. M., Hassanzadeh, M., Anagnostopoulos, A., et al. (2025). A thorough assessment of the non-iid data impact in federated learning. Available at: [link].
Jiménez-Gutiérrez, D. M., Solans, D., Heikkilä, M., et al. (2024). Non-iid data in federated learning: A survey with taxonomy, metrics, methods, frameworks and future directions. Available at: [link].
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in ML, 14(1–2):1–210.
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S. U., and Suresh, A. T. (2020). Scaffold: Stochastic controlled averaging for federated learning. In ICML.
Köppel, M., Segner, A., Wagener, M., Pensel, L., Karwath, A., and Kramer, S. (2019). Pairwise learning to rank by neural networks revisited: Reconstruction, theoretical analysis and practical performance. arXiv preprint arXiv:1909.02768.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and anovas. Frontiers in Psychology, Volume 4 - 2013.
Lee, G., Jeong, M., Shin, Y., Bae, S., and Yun, S.-Y. (2022). Preservation of the global knowledge by not-true distillation in federated learning.
Li, C. and Ouyang, H. (2021). Federated unbiased learning to rank.
Li, Q., He, B., and Song, D. (2021). Model-contrastive federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020). Federated optimization in heterogeneous networks. In Proceedings of Machine Learning and Systems, pages 429–450.
Liu, J., Huang, J., et al. (2021). From distributed machine learning to federated learning: A survey. Available at: [link].
Liu, T.-Y. (2010). Learning to rank for information retrieval. In ACM SIGIR, page 904.
Lv, Y., Ding, H., Wu, H., Zhao, Y., and Zhang, L. (2023). Fedrds: Federated learning on non-iid data via regularization and data sharing. Applied Sciences, 13(23).
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2023). Communication-efficient learning of deep networks from decentralized data.
Mukut, S., Kakoli, G., and Jyotika, B. (2012). Federated search: An information retrieval strategy for scholarly literature.
Neto, H. N. C., Mattos, D. M. F., and Fernandes, N. C. (2020). Privacidade do usuário em aprendizado colaborativo: Federated learning, da teoria à prática. In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSEG).
Qin, T. and Liu, T. (2013). Introducing LETOR 4.0 datasets. CoRR, abs/1306.2597.
Rodrigues, P. H., Danielde Sousa, França, C., Rabbi, G., Rosa, T., and Gonçalves, M. A. (2025). Risk-sensitive optimization of neural deep learning ranking models with applications in ad-hoc retrieval and recommender systems. IP&M, 62(4):104126.
Rodrigues, P. H. S., Xavier Sousa, D., Couto Rosa, T., and Gonçalves, M. A. (2022). Risk-sensitive deep neural learning to rank. In ACM SIGIR, page 803–813.
Spiegelhalter, D. (2024). The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck. Pelican Books.
Voorhees, E. M. (1999). The trec-8 question answering track report. In TREC-8. National Institute of Standards and Technology (NIST).
Wang, H., Xu, H., Li, Y., Xu, Y., Li, R., and Zhang, T. (2024). FedCDA: Federated learning with cross-rounds divergence-aware aggregation. In ICLR.
Wang, J. and Liu, M. (2020). Tackling the objective inconsistency problem in heterogeneous federated optimization. In NeurIPS.
Wang, S. (2024). Effective and secure federated online learning to rank. arXiv preprint arXiv:2412.19069.
Wang, S. and Zuccon, G. (2022). Is non-iid data a threat in federated online learning to rank? In ACM SIGIR Conference, SIGIR ’22, page 2801–2813.
Wang, Y., Li, T.-Y., Wang, D., and Zhu, M. (2013). A theoretical analysis of ndcg type ranking measures. Journal of Machine Learning Research, 14:25–54.
Yu, T., Bagdasaryan, E., and Shmatikov, V. (2022). Salvaging federated learning by local adaptation.
Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, T. N., and Khazaeni, Y. (2019). Bayesian nonparametric federated learning of neural networks.
Zhao, S. et al. (2024). Federated risk-aware learning with central sensitivity estimation. arXiv preprint arXiv:2502.17694.
Zhu, H., Jin, B., Li, H., and Liang, X. (2021). Federated learning on non-iid data: A survey. Neurocomputing, 465:371–390.
Publicado
25/05/2026
Como Citar
RABBI, Gestefane; FRANÇA, Celso; ROSA, Thierson Couto; ALMEIDA, Jussara M.; SOUSA, Daniel Xavier de; GONÇALVES, Marcos André.
Aprendizado Federado Incremental e Sensível ao Risco para Modelos de Ranqueamento em Cenários com Distribuições Heterogêneas de Dados. In: CONCURSO DE TESES, DISSERTAÇÕES E TCCS EM SI - MESTRADO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 22. , 2026, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 26-41.
DOI: https://doi.org/10.5753/sbsi_estendido.2026.249072.
