Abstract
Federated Learning (FL) and the Internet of Things (IoT) have transformed data processing and analysis, overcoming traditional cloud computing limitations. However, challenges such as catastrophic forgetting in continuous training scenarios arise. To address these, we propose an FL framework that supports continual learning while enhancing system security. We preserve critical knowledge through the incorporation of Knowledge Distillation (KD), addressing the issue of catastrophic forgetting. In addition, we have integrated encryption techniques to secure the updated parameters of clients from potential threats posed by attackers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nguyen, D.C., et al.: Federated learning meets blockchain in edge computing: opportunities and challenges. IEEE Internet Things J. 8(16), 12806–12825 (2021)
Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutor. 19(4), 2322–2358 (2017)
Park, T.J., Kumatani, K., Dimitriadis, D.: Tackling dynamics in federated incremental learning with variational embedding rehearsal. arXiv preprint arXiv:2110.09695 (2021)
Usmanova, A., Portet, F., Lalanda, P., Vega, G.: A distillation-based approach integrating continual learning and federated learning for pervasive services. arXiv preprint arXiv:2109.04197 (2021)
Mitchell, T., et al.: Never-ending learning. Commun. ACM 61(5), 103–115 (2018)
Van de Ven, G.M., Siegelmann, H.T., Tolias, A.S.: Brain-inspired replay for continual learning with artificial neural networks. Nat. Commun. 11(1), 4069 (2020)
Hadsell, R., Rao, D., Rusu, A.A., Pascanu, R.: Embracing change: continual learning in deep neural networks. Trends Cogn. Sci. 24(12), 1028–1040 (2020)
Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54–71 (2019)
Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A.D., van de Weijer, J.: Class-incremental learning: survey and performance evaluation on image classification. IEEE Trans. Pattern Anal. Mach. Intell. 45, 5513–5533 (2022)
Criado, M.F., Casado, F.E., Iglesias, R., Regueiro, C.V., Barro, S.: Non-IID data and continual learning processes in federated learning: a long road ahead. Inf. Fusion 88, 263–280 (2022)
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 (2017)
Mothukuri, V., Parizi, R.M., Pouriyeh, S., Huang, Y., Dehghantanha, A., Srivastava, G.: A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 115, 619–640 (2021)
Schwarz, J., et al.: Progress & compress: a scalable framework for continual learning. In: International Conference on Machine Learning, pp. 4528–4537. PMLR (2018)
Zhu, K., Zhai, W., Cao, Y., Luo, J., Zha, Z.J.: Self-sustaining representation expansion for non-exemplar class-incremental learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9296–9305 (2022)
Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521–3526 (2017)
van de Ven, G.M., Tuytelaars, T., Tolias, A.S.: Three types of incremental learning. Nat. Mach. Intell. 4, 1185–1197 (2022). https://doi.org/10.1038/s42256-022-00568-3
Acknowledgement
This work is partially supported by JSPS international scientific exchanges between Japan and India (Bilateral Program DTS-JSPS) (2022–2024). The research of the first author is partially supported by the Japan Science and Technology Agency, Support for Pioneering Research Initiated by the Next Generation (JST SPRING) under Grant JPMJSP2136. The research of the second author is partially supported by the International Exchange, Foreign Researcher Invitation Program of National Institute of Information and Communications Technology (NICT), Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, C., Wang, K.IK., Li, P., Sakurai, K. (2023). POSTER: Advancing Federated Edge Computing with Continual Learning for Secure and Efficient Performance. In: Zhou, J., et al. Applied Cryptography and Network Security Workshops. ACNS 2023. Lecture Notes in Computer Science, vol 13907. Springer, Cham. https://doi.org/10.1007/978-3-031-41181-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-41181-6_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-41180-9
Online ISBN: 978-3-031-41181-6
eBook Packages: Computer ScienceComputer Science (R0)