Balancing Privacy and Accuracy in Federated Learning for Speech Emotion Recognition

• Autorzy: Mohammadi Samaneh , Mohammadi Mohammadreza , Sinaei Sima , Balador Ali , Nowroozi Ehsan , Flammini Francesco , Conti Mauro

Identyfikatory

DOI

10.15439/2023F444

• Języki publikacji: EN

Abstrakty

EN

Speech Emotion Recognition (SER) is a valuable technology that identifies human emotions from spoken language, enabling the development of context-aware and personalized intelligent systems. To protect user privacy, Federated Learning (FL) has been introduced, enabling local training of models on user devices. However, FL raises concerns about the potential exposure of sensitive information from local model parameters, which is especially critical in applications like SER that involve personal voice data. Local Differential Privacy (LDP) has been successful in preventing privacy leaks in image and video data. However, it encounters notable accuracy degradation when applied to speech data, especially in the presence of high noise levels. In this paper, we propose an approach called LDP-FL with CSS, which combines LDP with a novel client selection strategy (CSS). By leveraging CSS, we aim to improve the representatives of updates and mitigate the adverse effects of noise on SER accuracy while ensuring client privacy through LDP. Furthermore, we conducted model inversion attacks to evaluate the robustness of LDP-FL in preserving privacy. These attacks involved an adversary attempting to reconstruct individuals' voice samples using the output labels provided by the SER model. The evaluation results reveal that LDP-FL with CSS achieved an accuracy of 65-70\%, which is 4\% lower than the initial SER model accuracy. Furthermore, LDP-FL demonstrated exceptional resilience against model inversion attacks, outperforming the non-LDP method by a factor of 10. Overall, our analysis emphasizes the importance of achieving a balance between privacy and accuracy in accordance with the requirements of the SER application.

Słowa kluczowe

EN

federated learning; privacy-preserving mechanism; differential privacy; speech emotion recognition

PL

uczenie federacyjne; ochrona prywatności; prywatność różnicowa; rozpoznawanie emocji mowy

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 191--199
• Opis fizyczny: Bibliogr. 26 poz., rys., wykr.

Twórcy

autor

Mohammadi Samaneh

• RISE Research Institutes of Sweden, Västerås, Sweden
• Mälardalen University, Västerås, Sweden

autor

Mohammadi Mohammadreza

• RISE Research Institutes of Sweden, Västerås, Sweden
• University of Padua, Padua, Italy

autor

Sinaei Sima

• RISE Research Institutes of Sweden, Västerås, Sweden

autor

Balador Ali

• Mälardalen University, Västerås, Sweden

autor

Nowroozi Ehsan

• Queen’s University Belfast, Centre of Secure Information Technologies, Belfast, Northern Ireland, United Kingdom.

autor

Flammini Francesco

• Mälardalen University, Västerås, Sweden

autor

Conti Mauro

• University of Padua, Padua, Italy

Bibliografia

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Uwagi

1. Main Track Regular Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).