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Optimized Paillier Homomorphic Encryption in Federated Learning for Speech Emotion Recognition
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.ORCID iD: 0000-0003-4725-0595
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0001-5951-9374
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.ORCID iD: 0000-0002-4473-7763
Mälardalen University, Sweden.
2023 (English)In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), 2023, p. 1021-1022Conference paper, Published paper (Refereed)
Abstract [en]

Federated Learning is an approach to distributed machine learning that enables collaborative model training on end devices. FL enhances privacy as devices only share local model parameters instead of raw data with a central server. However, the central server or eavesdroppers could extract sensitive information from these shared parameters. This issue is crucial in applications like speech emotion recognition (SER) that deal with personal voice data. To address this, we propose Optimized Paillier Homomorphic Encryption (OPHE) for SER applications in FL. Paillier homomorphic encryption enables computations on ciphertext, preserving privacy but with high computation and communication overhead. The proposed OPHE method can reduce this overhead by combing Paillier homomorphic encryption with pruning. So, we employ OPHE in one of the use cases of a large research project (DAIS) funded by the European Commission using a public SER dataset.

Place, publisher, year, edition, pages
2023. p. 1021-1022
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-66346DOI: 10.1109/compsac57700.2023.00156OAI: oai:DiVA.org:ri-66346DiVA, id: diva2:1794741
Conference
IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)
Note

This work was partially supported by EU ECSEL project DAIS that has received funding from the ECSEL JU under grant agreement No.101007273.

Available from: 2023-09-06 Created: 2023-09-06 Last updated: 2024-03-13Bibliographically approved

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Mohammadi, SamanehSinaei, SimaBalador, Ali

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