Concept Drift Detection and Adaptation for Robotics and Mobile Devices in Federated and Continual Settings

Author:

Casado Fernando E.,Lema Dylan,Iglesias Roberto,Regueiro Carlos V.,Barro Senén

Publisher

Springer International Publishing

Reference14 articles.

1. Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under conceptdrift: a review. IEEE Trans. Knowl. Data Eng. 31, 2346–2363 (2018)

2. Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Díaz-Rodríguez, N.: Continual learning for robotics: definition, framework, learning strategies, opportunities and challenges. Inf. Fusion 58, 52–68 (2020)

3. Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continuallifelong learning with neural networks: a review. Neural Networks (2019)

4. McMahan, H.B., Moore, E., Ramage, D., Aguera-Arcas, B.: Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629v1 (2016)

5. Li, Q., Wen, Z., He, B.: Federated learning systems: vision, hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693 (2019)

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