Abstract
Speech Emotion Recognition (SER) detects human emotions expressed in spoken language. SER is highly valuable in diverse fields; however, privacy concerns arise when analyzing speech data, as it reveals sensitive information like biometric identity. To address this, Federated Learning (FL) has been developed, allowing models to be trained locally and just sharing model parameters with servers. However, FL introduces new privacy concerns when transmitting local model parameters between clients and servers, as third parties could exploit these parameters and disclose sensitive information. In this paper, we introduce a novel approach called Secure and Efficient Federated Learning (SEFL) for SER applications. Our proposed method combines Paillier homomorphic encryption (PHE) with a novel gradient pruning technique. This approach enhances privacy and maintains confidentiality in FL setups for SER applications while minimizing communication and computation overhead and ensuring model accuracy. As far as we know, this is the first paper that implements PHE in FL setup for SER applications. Using a public SER dataset, we evaluated the SEFL method. Results show substantial efficiency gains with a key size of 1024, reducing computation time by up to 25% and communication traffic by up to 70%. Importantly, these improvements have minimal impact on accuracy, effectively meeting the requirements of SER applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
DAIS Project Website: https://dais-project.eu/.
References
Acar, A., Aksu, H., Uluagac, A.S., Conti, M.: A survey on homomorphic encryption schemes: theory and implementation. ACM Comput. Surv. (CSUR) 51(4), 1–35 (2018)
Cao, H., Cooper, D.G., Keutmann, M.K., Gur, R.C., Nenkova, A., Verma, R.: CREMA-D: crowd-sourced emotional multimodal actors dataset. IEEE Trans. Affect. Comput. (CSUR) 5(4), 377–390 (2014)
Fang, C., Guo, Y., Hu, Y., Ma, B., Feng, L., Yin, A.: Privacy-preserving and communication-efficient federated learning in internet of things. Comput. Secur. 103, 102199 (2021)
Feng, T., Peri, R., Narayanan, S.: User-level differential privacy against attribute inference attack of speech emotion recognition in federated learning. arXiv preprint arXiv:2204.02500 (2022)
Flammini, F., Alcaraz, C., Bellini, E., Marrone, S., Lopez, J., Bondavalli, A.: Towards trustworthy autonomous systems: taxonomies and future perspectives. IEEE Trans. Emerg. Top. Comput. 1–13 (2022). https://doi.org/10.1109/TETC.2022.3227113
Jere, M.S., Farnan, T., Koushanfar, F.: A taxonomy of attacks on federated learning. IEEE Secur. Priv. 19(2), 20–28 (2020)
Jiang, Y., et al.: Model pruning enables efficient federated learning on edge devices. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Jiang, Z., Wang, W., Liu, Y.: FLASHE: additively symmetric homomorphic encryption for cross-silo federated learning. arXiv preprint arXiv:2109.00675 (2021)
Khalil, R.A., Jones, E., Babar, M.I., Jan, T., Zafar, M.H., Alhussain, T.: Speech emotion recognition using deep learning techniques: a review. IEEE Access 7, 117327–117345 (2019)
Kröger, J.L., Lutz, O.H.-M., Raschke, P.: Privacy implications of voice and speech analysis – information disclosure by inference. In: Friedewald, M., Önen, M., Lievens, E., Krenn, S., Fricker, S. (eds.) Privacy and Identity 2019. IAICT, vol. 576, pp. 242–258. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42504-3_16
Latif, S., Khalifa, S., Rana, R., Jurdak, R.: Federated learning for speech emotion recognition applications. In: 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 341–342. IEEE (2020)
Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., Lu, R.: Privacy-enhanced federated learning against poisoning adversaries. IEEE Trans. Inf. Forensics Secur. 16, 4574–4588 (2021)
Ma, X., Lin, S., Ye, S., He, Z., Zhang, L., Yuan, G., Tan, S.H., Li, Z., Fan, D., Qian, X., et al.: Non-structured DNN weight pruning-is it beneficial in any platform? IEEE Trans. Neural Netw. Learn. Syst. 33(9), 4930–4944 (2021)
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)
Park, J., Lim, H.: Privacy-preserving federated learning using homomorphic encryption. Appl. Sci. 12(2), 734 (2022)
Tsouvalas, V., Ozcelebi, T., Meratnia, N.: Privacy-preserving speech emotion recognition through semi-supervised federated learning. In: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), pp. 359–364. IEEE (2022)
Tuncer, T., Dogan, S., Acharya, U.R.: Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques. Knowl.-Based Syst. 211, 106547 (2021)
Voigt, P., Von dem Bussche, A.: The EU General Data Protection Regulation (GDPR). A Practical Guide, 1st edn., vol. 10, no. 3152676, p. 10–5555. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57959-7
Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., Liu, Y.: \(\{\)BatchCrypt\(\}\): efficient homomorphic encryption for \(\{\)Cross-Silo\(\}\) federated learning. In: 2020 USENIX Annual Technical Conference (USENIX ATC 2020), pp. 493–506 (2020)
Zhang, J., Chen, B., Yu, S., Deng, H.: PEFL: a privacy-enhanced federated learning scheme for big data analytics. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2019)
Acknowledgement and Disclaimer
This work was partially supported by EU ECSEL project DAIS which has received funding from the ECSEL JU under grant agreement No.101007273. The work reflects only the authors’ views; the European Commission is not responsible for any use that may be made of the information it contains.
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 Singapore Pte Ltd.
About this paper
Cite this paper
Mohammadi, S., Sinaei, S., Balador, A., Flammini, F. (2023). Secure and Efficient Federated Learning by Combining Homomorphic Encryption and Gradient Pruning in Speech Emotion Recognition. In: Meng, W., Yan, Z., Piuri, V. (eds) Information Security Practice and Experience. ISPEC 2023. Lecture Notes in Computer Science, vol 14341. Springer, Singapore. https://doi.org/10.1007/978-981-99-7032-2_1
Download citation
DOI: https://doi.org/10.1007/978-981-99-7032-2_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7031-5
Online ISBN: 978-981-99-7032-2
eBook Packages: Computer ScienceComputer Science (R0)