Abstract
The massive datasets are often collected under non-IID distribution scenarios, which enforces existing federated learning (FL) frameworks to be still struggling on the model accuracy and convergence. To achieve heterogeneity-aware collaborative training, the FL server aggregates gradients from different clients to ingest and transfer common knowledge behind non-IID data, while leading to information loss and bias due to statistical weighting. To address the above issues, we propose a Gradient Memory-based Federated Learning (GradMFL) framework, which enables Hierarchical Knowledge Transferring over Non-IID Data. In GradMFL, a data clustering method is proposed to categorize Non-IID data to IID data according to the similarity. And then, in order to enable beneficial knowledge transferring between hierarchical clusters, we also present a multi-stage model training mechanism using gradient memory, constraining the updating directions. Experiments on solving a set of classification tasks based on benchmark datasets have shown the strong performance of good accuracy and high efficiency.
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
Yan, Z., Wu, J., Li, G., Li, S., Guizani, M.: Deep neural backdoor in semi-supervised learning: threats and countermeasures. IEEE Trans. Inf. Forensics Secur. 16, 4827–4842 (2021). https://doi.org/10.1109/TIFS.2021.3116431
Huang, X., Leng, S., Maharjan, S., Zhang, Y.: Multi-agent deep reinforcement learning for computation offloading and interference coordination in small cell networks. IEEE Trans. Veh. Technol. 70(9), 9282–9293 (2021). https://doi.org/10.1109/TVT.2021.3096928
Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., Yu, H.: Federated learning. Synth. Lect. Artif. Intell. Mach. Learn. 13(3), 1–207 (2019)
Wu, Y., Zhang, K., Zhang, Y.: Digital twin networks: a survey. IEEE Internet Things J. 8(18), 13789–13804 (2021). https://doi.org/10.1109/JIOT.2021.3079510
McMahan, H.B., Moore, E., Ramage, D., Arcas, B.A.Y.: Federated learning of deep networks using model averaging. CoRR abs/1602.05629 (2016)
Kairouz, P., et al.: Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)
Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization. arXiv preprint arXiv:2007.07481 (2020)
Nielsen, F.: Hierarchical clustering. In: Introduction to HPC with MPI for Data Science. UTCS, pp. 195–211. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-21903-5_8
Pfülb, B., Gepperth, A., Abdullah, S., Kilian, A.: Catastrophic forgetting: still a problem for DNNs. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 487–497. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01418-6_48
Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA, 4–9 December 2017, pp. 6467–6476 (2017)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 12:1-12:19 (2019). https://doi.org/10.1145/3298981
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018)
Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.: Federated multi-task learning. arXiv preprint arXiv:1705.10467 (2017)
Sattler, F., Müller, K.R., Samek, W.: Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans. Neural Netw. Learn. Syst. 32(8), 3710–3722 (2020)
Briggs, C., Fan, Z., Andras, P.: Federated learning with hierarchical clustering of local updates to improve training on non-IID data. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–9. IEEE (2020)
Delange, M., et al.: A continual learning survey: defying forgetting in classification tasks. IEEE Trans. Pattern Anal. Mach. Intell. (2021)
Kemker, R., McClure, M., Abitino, A., Hayes, T., Kanan, C.: Measuring catastrophic forgetting in neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. CoRR abs/1612.00796 (2016)
Xiao, T., Zhang, J., Yang, K., Peng, Y., Zhang, Z.: Error-driven incremental learning in deep convolutional neural network for large-scale image classification. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 177–186 (2014)
Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M.: Efficient lifelong learning with A-GEM. arXiv preprint arXiv:1812.00420 (2018)
Pan, Q., Wu, J., Bashir, A.K., Li, J., Yang, W., Al-Otaibi, Y.D.: Joint protection of energy security and information privacy for energy harvesting: An incentive federated learning approach. IEEE Trans. Ind. Inform. (2021). https://doi.org/10.1109/TII.2021.3105492
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., Gao, Y.: A survey on federated learning. Knowl.-Based Syst. 216, 106775 (2021)
Li, M., Li, J., Ou, Y., Zhang, Y., Luo, D., Bahtia, M., Cao, L.: Coupled K-nearest centroid classification for non-IID data. In: Nguyen, N.T., Kowalczyk, R., Corchado, J.M., Bajo, J. (eds.) Transactions on Computational Collective Intelligence XV. LNCS, vol. 8670, pp. 89–100. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44750-5_5
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)
Acknowledgement
This work is supported by National Natural Science Foundation of China under Grant No. U20B2048 and 61972255, Shanghai Sailing Program under Grant No. 21YF1421700, Special Fund for Industrial Transformation and Upgrading Development of Shanghai Under Grant No. GYQJ-2018-3-03 and Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0102.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Tong, G., Li, G., Wu, J., Li, J. (2022). GradMFL: Gradient Memory-Based Federated Learning for Hierarchical Knowledge Transferring Over Non-IID Data. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-95384-3_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95383-6
Online ISBN: 978-3-030-95384-3
eBook Packages: Computer ScienceComputer Science (R0)