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Este trabalho investiga o impacto de distribuições de dados não-Independente e Identicamente Distribuída (não-IID) no desempenho de Sistemas de Recomendação (SR) de filmes utilizando Aprendizagem Federada, em inglês Federated Learning (FL). Através da colaboração entre partes, o FL permite a distribuição do custo computacional e amplia a diversidade de dados disponíveis para o processo de treinamento, favorecendo a construção de SRs com boa capacidade de generalização. Neste contexto, e com o objetivo de analisar a eficácia da estratégia de compartilhamento parcial de dados, conduziram-se experimentos em quatro cenários utilizando o dataset MovieLens1M: (C1) dados IID; (C2) dados não-IID sem compartilhamento; (C3) dados não-IID com 1%  de compartilhamento; e (C4) dados não-IID com 5% de compartilhamento. Dois paradigmas federados foram utilizados: Cross-Silo e Cross-Device. Resultados experimentais indicam que o compartilhamento parcial de dados é uma abordagem promissora para mitigar os efeitos adversos de distribuições não-IID em Aprendizagem Federada, incorrendo em aumentos de aproximadamente 9,5% para NDCG e quedas de 0,046 no MAE, por exemplo. Assim sendo, equilibra-se desempenho preditivo, privacidade e custo computacional.

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This work investigates the impact of non-independent and identically distributed data distributions (non-IID) on the performance of movies Recommender Systems (SR) using Federated Learning (FL). Through the collaboration between parts, FL allows the distribution of computational cost and expands the data diversity available to the training process, favoring the construction of SRs with good generalization capacity. In this context, and to analyze the effectiveness of the partial data sharing strategy, experiments were conducted in four scenarios using the dataset Movielens1M: (C1) IID data; (C2) non-sharing non-IID data; (C3) non-IID data with 1% sharing; and (C4) non-IID data with 5% sharing. Two federated paradigms were used: cross-silo and cross-device. Experimental results indicate that partial data sharing is a promising approach to mitigate the adverse effects of non-IID distributions in federated learning, incurring approximately 9,5% increases for NDCG and 0.046 drops in the MAE, for example. Thus, predictive performance, privacy, and computational cost are balanced.

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Authors

#	Name
1	Arthur Martins da Costa(arthur.negrao@aluno.ufop.edu.br)
2	Guilherme Rocha(guilherme.rocha@aluno.ufop.edu.br)
3	Lucas Dos Santos(lucas.gs1@aluno.ufop.edu.br)
4	Pedro Malaquias(pedro.malaquias@aluno.ufop.edu.br)
5	Rodrigo Silva(rodrigo.silva@ufop.edu.br)
6	Reinaldo Fortes(reifortes@ufop.edu.br)
7	Pedro Henrique Lopes Silva(silvap@ufop.edu.br)

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Reference

#	Reference
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35	McMahan, H. B., Moore, E., Ramage, D., and y Arcas, B. A. (2016). Federated learning of deep networks using model averaging. CoRR, abs/1602.05629.
36	Shahbazi, Z. and Byun, Y.-C. (2019). Product recommendation based on content-based filtering using xgboost classifier. Int. J. Adv. Sci. Technol, 29:6979–6988.
37	Sharma, L. and Gera, A. (2013). A survey of recommendation system: Research challenges. International Journal of Engineering Trends and Technology (IJETT), 4(5):1989–1992.
38	Shwartz-Ziv, R. and Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion, 81:84–90.
39	ur Rehman, M. H., Dirir, A. M., Salah, K., Damiani, E., and Svetinovic, D. (2021). Trustfed: A framework for fair and trustworthy cross-device federated learning in iiot. IEEE Transactions on Industrial Informatics, 17(12):8485– 8494.
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43	Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V. (2018). Federated learning with non-iid data. arXiv preprint arXiv:1806.00582.
44	Zhu, H., Xu, J., Liu, S., and Jin, Y. (2021). Federated learning on non-iid data: A survey. Neurocomputing, 465:371–390.
45	Zhu, S., Zeng, J., Wang, S., Sun, Y., Li, X., Yao, Y., and Peng, Z. (2024). On admm in heterogeneous federated learning: Personalization, robustness, and fairness. arXiv preprint arXiv:2407.16397.

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