[1] Li, C., Niu, D., Jiang, B., Zuo, X., Yang, J.:. Meta-har: Federated representation learning for human activity recognition. In: Proceedings of the Web Conference 2021, pp. 912–922, April 2021

[2] Kong, Y., Fu, Y.: Human action recognition and prediction: a survey. Int. J. Comput. Vision 130(5), 1366–1401 (2022)

[3] Li, Y., Yang, G., Su, Z., Li, S., Wang, Y.: Human activity recognition based on multienvironment sensor data. Inf. Fusion 91, 47–63 (2023)

[4] Karim, M., Khalid, S., Aleryani, A., Khan, J., Ullah, I., Ali, Z.: Human action recognition systems: a review of the trends and state-of-the-art. IEEE Access (2024).

[5] McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR, April 2017

[6] Konečný, J., McMahan, H.B., Ramage, D., Richtárik, P.: Federated optimization: Distributed machine learning for on-device intelligence (2016). arXiv preprint arXiv:1610.02527

[7] Jung, I.Y.: A review of privacy-preserving human and human activity recognition. Int. J. Smart Sens. Intell. Syst. 13(1), 1–13 (2020)

[8] Sozinov, K., Vlassov, V., Girdzijauskas, S.: Human activity recognition using federated learning, pp. 1103–1111 (2018). https://doi.org/10.1109/BDCloud.2018.00164

[9] Kondratyuk, D., et al.: Movinets: mobile video networks for efficient video recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16020–16030 (2021)

[10] Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429–450 (2020)

[11] Qi, P., Chiaro, D., Piccialli, F.: FL-FD: federated learning-based fall detection with multimodal data fusion. Inf. Fusion 99, 101890 (2023)

[12] Hussain, A., Khan, S.U., Khan, N., Rida, I., Alharbi, M., Baik, S.W.: Low-light aware framework for human activity recognition via optimized dual stream parallel network. Alex. Eng. J. 74, 569–583 (2023)

[13] Hsu, T.M.H., Qi, H., Brown, M.: Measuring the effects of non-identical data distribution for federated visual classification (2019). arXiv preprint arXiv:1909.06335

[14] Reddi, S., et al.: Adaptive federated optimization (2020). arXiv preprint arXiv:2003.00295

[15] Soomro, K.: UCF101: a dataset of 101 human actions classes from videos in the wild (2012). arXiv preprint arXiv:1212.0402