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English Information

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Authors
# Name
1 Camila Lopes(camila_ol@id.uff.br)
2 Alan Nunes(Alan_lira@id.uff.br)
3 Cristina Boeres(boeres@ic.uff.br)
4 Lúcia Drummond(lucia@ic.uff.br)
5 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 Beutel, D. J. et al. (2020). Flower: A Friendly Federated Learning Research Framework. arXiv preprint arXiv:2007.14390.
2 Chapman, A., Lauro, L., Missier, P., and Torlone, R. (2022). DPDS: assisting data science with data provenance. Proc. VLDB Endow., 15(12):3614–3617.
3 Dwork, C. (2006). Differential privacy. 33rd ICALP 2006, Proceedings, Part II, volume 4052, pages 1–12. Springer.
4 Freire, J., Koop, D., Santos, E., and Silva, C. T. (2008). Provenance for Computational Tasks: A Survey. Computing in Science & Engineering, 10(3):11–21.
5 Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press
6 Groth, P. and Moreau, L. (2013). W3C PROV - An Overview of the PROV Family of Documents. Available at https://www.w3.org/TR/ prov-overview/.
7 Krizhevsky, A. (2009). Learning Multiple Layers of Features from Tiny Images. Technical report, University of Toronto.
8 Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. (2020). Federated Optimization in Heterogeneous Networks. Proceedings of Machine Learning and Systems (MLSys). mlsys.org.
9 McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proc. of 20th AISTATS, pages 1273–1282.
10 Peregrina, J.A.,Ortiz,G.,andZirpins,C.(2022). Towards a Metadata Management System for Provenance, Reproducibility and Accountability in Federated Machine Learning. Advances in Service-Oriented and Cloud Computing, pages 5–18. Springer.
11 Pina, D., Chapman, A., Oliveira, D., and Mattoso, M. (2023). Deep learning provenance data integration: a practical approach. IPAW, pages 1542–1550. ACM.
12 Sandler, M., A. Howard, M. Z., Zhmoginov, A., and Chen, L. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4510–4520.
13 Silva, V., Campos, V., Guedes, T., Camata, J., de Oliveira, D., Coutinho, A. L., Valduriez, P., and Mattoso, M. (2020). Dfanalyzer: Runtime dataflow analysis tool for computational science and engineering applications. SoftwareX, 12:100592.