Abstract
Federated Learning (FL) has recently attracted considerable attention in multi-robot collaborative systems, owning to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server. In a multi-robot collaboration system, an approach that ensures privacy-preserving knowledge sharing among multiple robots becomes imperative. However, the application of FL in such systems encounters two major challenges. Firstly, it is inefficient to use all the network nodes as federated learning clients (which conduct training of machine learning model based on own data) due to the limited wireless bandwidth and energy of robots. Secondly, the selection of an appropriate number of clients must be carefully considered, considering the constraints imposed by limited communication resources. Selecting an excessive number of clients may result in a failure in uploading important models. To overcome these challenges, this paper proposes a client selection approach that considers multiple metrics including the data volume, computational capability, and network environment by integrating fuzzy logic and Q-learning. The experimental results validate the theoretical feasibility of the proposed approach. Further empirical data can be derived from training experiments on public datasets, enhancing the practical applicability of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the Annual Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 843–852 (2017)
Yadav, R., Zhang, W., Kaiwartya, O., Song, H., Yu, S.: Energy-latency tradeoff for dynamic computation offloading in vehicular fog computing. IEEE Trans. Veh. Technol. 69(12), 14198–14211 (2020)
McMahan, H.B., Ramage, D., Talwar, K., et al.: Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963 (2017)
Apple Differential Privacy Team. Learning with privacy at scale. In Apple Machine Learning Journal (2017)
Hartmann, F., Suh, S., Komarzewski, A., et al.: Federated learning for ranking browser history suggestions. arXiv preprint arXiv:1911.11807 (2019)
Zhu, H., Xu, J., Liu, S., et al.: Federated learning on non-IID data: a survey. Neurocomputing 465, 371–390 (2021)
Zhao, Y., Li, M., Lai, L., et al.: Federated learning with non-IID data. arXiv preprint arXiv:1806.00582 (2018)
IEEE J. Sel. Areas Commun. 41(4), 915–928 (2023). https://doi.org/10.1109/JSAC.2023.3242720
Zhang, W., Wang, X., Zhou, P., Wu, W., Zhang, X.: Client selection for federated learning with non-IID data in mobile edge computing. IEEE Access 9, 24462–24474 (2021)
Qu, Z., Duan, R., Chen, L., Xu, J., Lu, Z., Liu, Y.: Context-Aware online client selection for hierarchical federated learning. IEEE Trans. Parallel Distrib. Syst. 33(12), 4353–4367 (2022)
Huang, T., Lin, W., Shen, L., Li, K., Zomaya, A.Y.: Stochastic client selection for federated learning with volatile clients. IEEE Internet Things J. 9(20), 20055–20070 (2022)
Asad, M., Moustafa, A., Rabhi, F.A., Aslam, M.: THF: 3-way hierarchical framework for efficient client selection and resource management in federated learning. IEEE Internet Things J. 9(13), 11085–11097 (2022)
Shi, F., Hu, C., Lin, W., Fan, L., Huang, T., Wu, W.: VFedCS: optimizing client selection for volatile federated learning. IEEE Internet Things J. 9(24), 24995–25010 (2022)
Acknowledgments
This research was supported in part by the Inner Mongolia Science and Technology Key Project No. 2021GG0218, ROIS NII Open Collaborative Research 23S0601, and in part by JSPS KAKENHI Grant No. 21H03424.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ding, N., Peng, C., Lin, M., Lin, Y., Du, Z., Wu, C. (2024). Client Selection Method for Federated Learning in Multi-robot Collaborative Systems. In: Wu, C., Chen, X., Feng, J., Wu, Z. (eds) Mobile Networks and Management. MONAMI 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-55471-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-55471-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-55470-4
Online ISBN: 978-3-031-55471-1
eBook Packages: Computer ScienceComputer Science (R0)