|
Bibliografía básica
|
Bahri, M., Bifet, A., Gama, J., Gomes, H. M., & Maniu, S (2021). Data stream analysis: Foundations, major tasks and tools. Wiley nterdisciplinary Reviews: Data Mining and Knowledge Discovery,11(3)
Bifet, A., Gavalda, R., Holmes, G., & Pfahringer, B (2018). Machine learning for data streams: with practical examples in MOA. MIT Press
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation.. CM computing surveys(CSUR),46(4), 1-37
Gomes, H. M., Read, J., Bifet, A., Barddal, J. P., & Gama, J. (2019). Machine learning for streaming data: state of the art, challenges, and opportunities.. ACM SIGKDD Explorations Newsletter,21(2), 6-22
Hoi, S. C., Sahoo, D., Lu, J., & Zhao, P. (2021). Online learning: A comprehensive survey. Neurocomputing,459, 249-289.
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions.. IEEE signal processing magazine, 37(3), 50-60
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018). Learning under concept drift: A review.. IEEE Transactions on Knowledge and DataEngineering,31(12), 2346-2363
Orabona, F. (2019). A modern introduction to online learning.. arXivpreprint arXiv:1912.13213
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications.. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19
|
|
Bibliografía complementaria
|
AbdulRahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2020). A survey on federated learning: The journey fromcentralized to distributed on-site learning and beyond.. IEEE Internet of Things Journal, 8(7), 5476-5497
Bifet, A., Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM international conference ondata mining, pp. 443-448. Society for Indust
Bifet, A., & Gavalda, R. (2009). Adaptive learning from evolving data streams.. InAdvances in Intelligent Data Analysis VIII
(). ttps://federated.withgoogle.com/.
Gama, J., & Castillo, G. (2006). Learning with local drift detection.. Advanced Data Mining and Applications: Second International Conference,ADMA 2006, Xi’an, China, Augu
Gama, J., Medas, P., Castillo, G., & Rodrigues, P. (2004). Learning with drift detection. InBrazilian symposium on artificialintelligence(pp. 286-295). Springer, Berlin, Heidelberg.
Ghesmoune, M., Lebbah, M., & Azzag, H (2016). State-of-the-art on clustering data streams.. Big Data Analytics, 1, 1-27
Gomes, H. M., Montiel, J., Mastelini, S. M., Pfahringer, B., & Bifet, A. (2020). On ensemble techniques for data stream regression. In 2020International Joint Conference on Neural Networks (IJCNN) (pp. 1-8)
McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282).
Rahman, K. J., Ahmed, F., Akhter, N., Hasan, M., Amin, R., Aziz, K. E., ... & Islam, A. N. (2021). hallenges, applications and design aspects of federated learning: A survey.. IEEE Access,9, 124682-124700.
|
Asignaturas