Samaneh Mohammadi is an industrial Ph.D. student at the School of Innovation, Design,
and Engineering at Mälardalen University and RISE Research Institutes of Sweden. Samaneh received her Master’s degree in Information Technology Engineering from the University of Tehran in Iran in 2020. Her Master’s thesis focused on the “Anomaly detection in Dynamic Networks,” which use deep learning and inductive learning.
She is employed at Smart Industrial Automation unit at RISE Research Institutes of Sweden.
Her research interests include Edge Computing, Edge Artificial intelligence, Federated Learning, and Deep Learning.
Balancing Privacy and Performance in Emerging Applications of Federated Learning
Federated Learning (FL) ensures privacy by training models on distributed devices without sharing raw data. However, privacy concerns arise as adversaries can still extract information from model parameters. This is crucial for security-critical applications like speech emotion recognition (SER). Existing privacy mechanisms impact system performance, leading to slower responses and reduced accuracy. Balancing privacy and performance is essential, and this research aims to propose privacy-preserving methods for FL in SER while addressing performance challenges. This research focuses on finding the right combination of methods to safeguard sensitive information without compromising system efficiency.
Balancing Privacy and Accuracy in Federated Learning for Speech Emotion Recognition. Authors: Samaneh Mohammadi, Mohammadreza Mohammadi , Sima Sinaei, Ali Balador, Ehsan Nowroozi , Francesco Flammini, Mauro Conti
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