Federated Learning Framework for Privacy-Preserving Voice Analytics in Smart TV Ecosystems
DOI:
https://doi.org/10.22399/ijcesen.5119Keywords:
Federated Learning, Smart TV Ecosystem, Smart TV Ecosystem, Voice Analytics, Privacy Preservation, Edge Computing, Secure AggregationAbstract
Voice assistants are increasingly being embedded within smart TVs, which is enabling consumers to interact with these devices more seamlessly and easily. One of the main ways that voice-based services service a user is by collecting and sending voice data via audio recordings to centralized cloud servers for analysis. While users may benefit from the information generated by such services, they will also face significant risks to their privacy and data security. Because most of the existing methods for machine learning rely solely on centralized machine learning data sets, the chances that sensitive private data will be improperly disclosed increases substantially. To mitigate these problems, the research described herein describes a federated framework for performing voice analytics in a privacy-preserving manner within the smart television ecosystem. The proposed federated framework will permit multiple smart televisions to collaboratively develop and train voice recognition models and perform analytics tasks, without transmitting any of the users' raw voice recordings to a single central server. Instead of transmitting raw recordings, the users' smart televisions will develop and train models locally using their own voice recordings. The smart televisions then transmit only encrypted updates regarding their models to a global aggregation server that will combine or aggregate them to create an overall or universal model. Implementing a federated learning approach significantly decreases the risk of an improper disclosure of personal information, while providing a voice recognition and analytics model that has comparable accuracy and efficiency as a central model. Additionally, the federated framework will implement secure protocols for the aggregation and communication of voice data to increase the security of the voice data and its robustness to possible attacks.
References
[1] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017, April). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). Pmlr.
[2] Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37(3), 50-60.
[3] Kairouz, P., & McMahan, H. B. (2021). Advances and open problems in federated learning. Foundations and trends in machine learning, 14(1-2), 1-210.
[4] Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Seth, K. (2017, October). Practical secure aggregation for privacy-preserving machine learning. In proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1175-1191).
[5] Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., ... & Ramage, D. (2018). Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604.
[6] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
[7] Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ digital medicine, 3(1), 119.
[8] Casella, B., Riviera, W., Aldinucci, M., & Menegaz, G. (2023). MERGE: A model for multi-input biomedical federated learning. Patterns, 4(11).
[9] Gupta, S., Kumar, S., Chang, K., Lu, C., Singh, P., & Kalpathy-Cramer, J. (2023). Collaborative privacy-preserving approaches for distributed deep learning using multi-institutional data. RadioGraphics, 43(4), e220107.
[10] Singh, T. M., Reddy, C. K. K., Puttanapura, J., & Doss, S. Optimizing neural disorder treatment through federated learning and multi-institutional data collaboration. In Federated Learning for Neural Disorders in Healthcare 6.0 (pp. 120-157). CRC Press.
[11] Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2023). Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions. IEEE Internet of Things Journal, 11(5), 7374-7398.
[12] Teo, Z. L., Jin, L., Liu, N., Li, S., Miao, D., Zhang, X., ... & Ting, D. S. W. (2024). Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture. Cell Reports Medicine, 5(2).
[13] Sandhu, S. S., Gorji, H. T., Tavakolian, P., Tavakolian, K., & Akhbardeh, A. (2023). Medical imaging applications of federated learning. Diagnostics, 13(19), 3140.
[14] Ait Abdelmoula, I., Oufettoul, H., Lamrini, N., Motahhir, S., Mehdary, A., & El Aroussi, M. (2024). Federated learning for solar energy applications: A case study on real-time fault detection. Solar Energy, 282, 112942.
[15] Hossain, M. B., Shinde, R. K., Oh, S., Kwon, K. C., & Kim, N. (2024). A systematic review and identification of the challenges of deep learning techniques for undersampled magnetic resonance image reconstruction. Sensors, 24(3), 753.
[16] Preethi, P., Saravanan, T., Mohanraj, R., & Gayathri, P. G. (2024). A real-time environmental air pollution predictor model using a dense deep learning approach in IoT infrastruc- ture. GLOBAL NEST JOURNAL, 26(3).
[17] Pamulaparthyvenkata, S., Sharma, J., Dattangire, R., Vishwanath, M., Mulukuntla, S., Preethi, P., & Indhumathi, N. (2024, June). Deep Learning and EHR-Driven Image Processing Frame- work for Lung Infection Detection in Healthcare Applications. In 2024 15th International Con- ference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
[18] Raj, R. R. M., Saravanan, T., Preethi, P., & Ezhilarasi, I. (2022). Comparative evaluation of efficacy of therapeutic ultrasound and phonophoresis in myofascial pain dysfunction syn- drome. Journal of Indian Academy of Oral Medicine and Radiology, 34(3), 242-245.
[19] Raza, A. (2025). The application of artificial intelligence in credit risk evaluation: Obstacles and opportunities in path to financial justice. Center for Management Science Research, 3(2), 240-251.
[20] Chohan, M. A., Farooqi, M. A., Raza, A., Rasheed, M. N., & Shahzad, K. (2024). ARTIFICIAL INTELLIGENCE AND INTELLECTUAL PROPERTY RIGHTS: FROM CONTENT CREATION TO OWNERSHIP.
[21] Raza, A., & Bashir, N. (2023). Artificial intelligence as a creator and inventor: legal challenges and protections in copyright, patent, and trademark law. Artificial Intelligence as a Creator and Inventor: Legal Challenges and Protections in Copyright, Patent, and Trademark Law (De- cember 31, 2023).
[22] Singh, B. (2023). Software-Defined Data Centers: Innovations in Network Architecture for High Availability. Available at SSRN 5331661.
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