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.