Fetching the paper…
Reading the bibliography…
Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment.
Pacific Journal of mathematics 16
Armijo, L.: Minimization of functions having lipschitz continuous first partial derivatives · 1966
Earlier work this paper cites.
Neural networks 1
Grossberg, S.: Nonlinear neural networks: Principles, mechanisms, and architectures · 1988
Earlier work this paper cites.
Trends in cognitive sciences 3
French, R.M.: Catastrophic forgetting in connectionist networks · 1999
Earlier work this paper cites.
Canadian Journal of Statistics 27
Baron, M.: Convergence rates of change-point estimators and tail probabilities of the first-passage-time process · 1999
Earlier work this paper cites.
Computer 47
Gaff, B.M., Sussman, H.E., Geetter, J.: Privacy and big data · 2014
Earlier work this paper cites.
Wiley StatsRef: Statistics Reference Online (2014)
Bowman, K., Shenton, L.: Estimation: Method of moments · 2014
Earlier work this paper cites.
Sensors 14
Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.: Fusion of smartphone motion sensors for physical activity recognition · 2014
Earlier work this paper cites.
arXiv preprint arXiv:1511.03575 (2015)
Konečnỳ, J., McMahan, B., Ramage, D.: Federated optimization: Distributed optimization beyond the datacenter · 2015
Earlier work this paper cites.
arXiv preprint arXiv:1602.05629v1 (2016)
McMahan, H.B., Moore, E., Ramage, D., Aguera-Arcas, B.: Federated learning of deep networks using model averaging · 2016
Earlier work this paper cites.
Data Mining and Knowledge Discovery 30
Webb, G.I., Hyde, R., Cao, H., Nguyen, H.L., Petitjean, F.: Characterizing concept drift · 2016
Earlier work this paper cites.
In: Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 357–368. i6doc (2016)
Gepperth, A., Hammer, B.: Incremental learning algorithms and applications · 2016
Earlier work this paper cites.
In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 1652–1658 (2016)
Haque, A., Khan, L., Baron, M.: Sand: Semi-supervised adaptive novel class detection and classification over data stream · 2016
Cited alongside, same era.
arXiv preprint arXiv:1610.05492 (2016)
Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency · 2016
Cited alongside, same era.
IEEE Transactions on Knowledge and Data Engineering (2018)
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review · 2018
Cited alongside, same era.
arXiv preprint arXiv:1806.00582 (2018)
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-iid data · 2018
Cited alongside, same era.
arXiv preprint arXiv:1904.07734 (2019)
van de Ven, G.M., Tolias, A.S.: Three scenarios for continual learning · 2019
Later among the works it cites.
Information Fusion 58
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Díaz-Rodríguez, N.: Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges · 2020
Later among the works it cites.
In: Workshop of Physical Agents, pp. 79–93. Springer (2020)
Casado, F.E., Lema, D., Iglesias, R., Regueiro, C.V., Barro, S.: Concept drift detection and adaptation for robotics and mobile devices in federated and continual settings · 2020
Later among the works it cites.
IEEE Signal Processing Magazine 37
Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: Challenges, methods, and future directions · 2020
Later among the works it cites.
IEEE Access 8
Aledhari, M., Razzak, R., Parizi, R.M., Saeed, F.: Federated learning: A survey on enabling technologies, protocols, and applications · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Caldas, S., Wu, P., Li, T., Konečnỳ, J., McMahan, H.B., Smith, V., Talwalkar, A.: Leaf: A benchmark for federated settings · 2018
Cited alongside, same era.
arXiv preprint arXiv:1812.06127 (2018)
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks · 2018
Cited alongside, same era.
arXiv preprint arXiv:1811.03604 (2018)
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., Ramage, D.: Federated learning for mobile keyboard prediction · 2018
Cited alongside, same era.
arXiv preprint arXiv:1907.09693 (2019)
Li, Q., Wen, Z., He, B.: Federated learning systems: Vision, hype and reality for data privacy and protection · 2019
Cited alongside, same era.
Springer (2019)
Custers, B., Sears, A.M., Dechesne, F., Georgieva, I., Tani, T., van der Hof, S.: EU Personal Data Protection in Policy and Practice · 2019
Cited alongside, same era.
Neural Networks (2019)
Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: A review · 2019
Cited alongside, same era.
arXiv preprint arXiv:1912.04977 (2019)
Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning · 2019
Cited alongside, same era.
Deng, Y., Kamani, M.M., Mahdavi, M.: Adaptive personalized federated learning · 2020
Later among the works it cites.
arXiv preprint arXiv:2003.03196v4 (2020)
Yoon, J., Jeong, W., Lee, G., Yang, E., Hwang, S.J.: Federated continual learning with weighted inter-client transfer · 2020
Later among the works it cites.
arXiv preprint arXiv:2006.07129v2 (2020)
Casado, F.E., Lema, D., Iglesias, R., Regueiro, C.V., Barro, S.: Collaborative and continual learning for classification tasks in a society of devices · 2020
Later among the works it cites.
Sensors 20
Casado, F.E., Rodríguez, G., Iglesias, R., Regueiro, C.V., Barro, S., Canedo-Rodríguez, A.: Walking recognition in mobile devices · 2020
Later among the works it cites.
Tong, L.N., He, J.J., Peng, L.: CNN-based PD hand tremor detection using inertial sensor · 2021
Closest in time.