Samaneh Mohammadi, Industrial Doctoral Student


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. She is employed at Smart Industrial Automation unit, RISE Research Institutes of Sweden. Her research interests include Edge Computing, Edge Artificial intelligence, Federated Learning, and Deep Learning. She has been involved in the huge EU project called DAIS https://dais-project.eu/.

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.

 

Her research area focuses on Privacy-Preserving Federated learning. Federated learning allows a server to learn a machine learning model across multiple decentralized clients that privately store their own training data. In contrast with centralized ML approaches, FL saves computation to the server and does not require the clients to outsource their private data to the server. However, FL is not free of issues.  So, the model updates sent by the clients at each training epoch might leak information on the clients’ private data. Thus, she is working on preserving privacy in the FL system.

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Latest publications:

EncCluster: Bringing Functional Encryption in Federated Foundational Models (Dec 2024)
Vasileios Tsouvalas , Samaneh Mohammadi, Ali Balador, Tanir Ozcelebi , Francesco Flammini, Nirvana Meratnia
2024 Conference on Neural Information Processing Systems (NeurIPS 2024)

Balancing Privacy and Performance in Federated Learning: a Systematic Literature Review on Methods and Metrics (Mar 2024)
Samaneh Mohammadi, Ali Balador, Sima Sinaei, Francesco Flammini
Journal of Parallel and Distributed Computing (JPDC)

Hyperparameters Optimization for Federated Learning System: Speech Emotion Recognition Case Study (Oct 2023)
Kateryna Mishchenko , Samaneh Mohammadi, Mohammadreza Mohammadi , Sima Sinaei
The Eighth IEEE International Conference on Fog and Mobile Edge Computing (FMEC 2023)

Balancing Privacy and Accuracy in Federated Learning for Speech Emotion Recognition (Sep 2023)
Samaneh Mohammadi, Mohammadreza Mohammadi , Sima Sinaei, Ali Balador, Ehsan Nowroozi , Francesco Flammini, Mauro Conti
18th Conference on Computer Science and Intelligence Systems (FedCSIS 2023)

Optimized Paillier Homomorphic Encryption in Federated Learning for Speech Emotion Recognition (Aug 2023)
Samaneh Mohammadi, Sima Sinaei, Ali Balador, Francesco Flammini
IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC 2023)

Secure and Efficient Federated Learning by Combining Homomorphic Encryption and Gradient Pruning in Speech Emotion Recognition (Aug 2023)
Samaneh Mohammadi, Sima Sinaei, Ali Balador, Francesco Flammini
18th International Conference on Information Security Practice and Experience (ISPEC 2023)