Proceedings of International Conference on Applied Innovation in IT
2023/03/09, Volume 11, Issue 1, pp.119-125

Human Activity Recognition with Wearables using Federated Learning

Borche Jovanovski, Stefan Kalabakov, Daniel Denkovski, Valentin Rakovic, Bjarne Pfitzner, Orhan Konak, Bert Arnrich and Hristijan Gjoreski

Abstract: The increasing use of Wearable devices opens up the use of a wide range of applications. Using different models, these devices can be of great use in Human Activity Recognition (HAR), where the main goal is to process information obtained from sensors located in them, especially in eHealth. The high volume of data collected by various smart devices in contemporary ML scenarios, leads to higher processing consumption and in many cases results in compromised privacy. These shortcomings could be overcome by using Federated Learning (FL), a learning paradigm that allows for decentralized training of models such that user’s personal data does not need to ever leave their devices, which substantially reduces to possibility of a breach. This paper analyses the behaviour and performances of FL when applied to the context of HAR. The obtained results show that FL can achieve comparable performances to those of centralized Deep Learning, while facilitating improved data privacy and diversity, as well as fostering real-time continuous learning.

Keywords: Activity Recognition, Machine Learning, Federated Learning, Deep Learning, Human Activity Recognition, Deep Neural Network.

DOI: 10.25673/101927

Download: PDF


  1. A. Alsiddikya, W. Awwada, K. Bakarmana, H.Fouad, A. S.Hassanein, and A. M. Solimanc, “Priority-based data transmission using selective decision modes in wearable sensor based healthcare applications”, Computer Communications, 1 July 2020, pp. 43-51.
  2. Z. Xiao, X. Xu, H. Xing, F. Song, X. Wang, and B. Zhao, ”A federated learning system with enhanced feature extraction for human activity recognition”, Knowl. Based Syst. 2021, vol. 229, p. 107338.
  3. L. Tu, X. Ouyang, J. Zhou, Y. He, and G. Xing, “FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition”, in Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, Coimbra Portugal, 15-17 November 2021.
  4. Y. Liu, J. Peng, J. Kang, A. M. Iliyasu, D. Niyato (IEEE Member), and A. A. Abd El-Latif (IEEE Fellow), “A Secure Federated Learning Framework for 5G Networks”, IEEE Wireless Communication Magazine, 12 May 2020.
  5. M. Wasilewska, H. Bogucka, and A. Kliks, “Federated Learning for 5G Radio Spectrum Sensing”, Sensors 2022.
  6. K. Sozinov, V. Vlassov, and S. Girdzijauskas, “Human activity recognition using federated learning”, in Proceedings of the 2018 IEEE Intl Conf on Parallel, Distributed Processing with Applications, Ubiquitous Computing Communications, Big Data Cloud Computing, Social Computing Networking, Sustainable Computing Communications.
  7. S. Ek, F. Portet, P. Lalanda, and G. Vega, “A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison”, 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom), 25 May 2021.
  8. C. Bettini, G. Civitarese, and R. Presotto, ”Personalized Semi-Supervised Federated Learning for Human Activity Recognition”, arXiv 2021, arXiv:2104.08094.
  9. L. Tu, X. Ouyang, J. Zhou, Y. He, and G. Xing, “FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition”, in Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, Coimbra Portugal, 15-17 November 2021.
  10. C. Li, D. Niu, B. Jiang, X. Zuo, and J. Yang, “Meta-HAR: Federated Representation Learning for Human Activity Recognition”, in Proceedings of theWeb Conference 2021 (WWW’21); Association for Computing Machinery: Ljubljana, Slovenia.
  11. G.K. Gudur and S.K. Perepu, “Resource- constrained federated learning with heterogeneous labels and models for human activity recognition”, in Proceedings of the Deep Learning for Human Activity Recognition: Second InternationalWorkshop, DL-HAR 2020, Kyoto, Japan, 8 January 2021, Springer: Berlin/Heidelberg, Germany, 2021.
  12. X. Ouyang, Z. Xie, J. Zhou, J. Huang, and G. Xing, “ClusterFL: A Similarity-Aware Federated Learning System for Human Activity Recognition”, MobiSys ’21: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, June 2021.
  13. S. Kozina, H. Gjoreski, M. Gams, and M. Lustrek, “Three-layer Activity Recognition Combining Domain Knowledge and Meta- classification”, Journal of Medical and Biological Engineering, vol. 33(4), pp. 406-414.
  14. R. Dastres and M. Soori, “Artificial Neural Network Systems”, International Journal of Imaging and Robotics (IJIR), 2021, vol. 21 (2), pp.13-25.
  15. Al-Z. Malek, S. Almajali, and A. Awajan, “Experimental evaluation of a multi-layer feedforward artificial neural network classifier for network intrusion detection system”, International Conference on New Trends in Computing Sciences (ICTCS) 2017.
  16. J. Singh and R. Banerjee, “A Study on Single and Multi-layer Perceptron Neural Network”, 2019 3rd International Conference on Computing Methodologies and Communication (IC-CMC), 27-29 March 2019.
  17. Q. Li, Z. Wen, Z. Wu, and et al., “A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection”, IEEE Transactions on Knowledge and Data Engineering (TKDE), 5 Dec 2021.


       - Call for Papers
       - Paper Submission
       - For authors
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


       - Volume 12, Issue 1 (ICAIIT 2024)        - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


       ICAIIT 2024
         - Photos
         - Reports

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports







         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0

                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

           ISSN 2199-8876
           Publisher: Anhalt University of Applied Sciences

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.