MSc Thesis Defense Announcement of Hamid Fazli Khojir:"Practical Secure Aggregation in Federated Learning using Additive Secret Sharing"

Wednesday, January 11, 2023 - 09:30 to 11:00


The School of Computer Science is pleased to present… 

MSc Thesis Defense by: Hamid Fazli Khojir 

Date: Wednesday, January 11, 2023 
Time:  9:30 am – 11:00 am 
Location: Essex Hall, Room 122 
Reminder: Recording of your attendance is mandatory - Part I: QR Code, Part II: Sign-in sheet.


Deep learning has enabled many industries to use data and train models with unlimited applications. However, data can include sensitive and private information of individuals, companies, or even hospitals. Therefore, data cannot simply be shared with a third party for training the model as it breaks the privacy of data owners, which is strongly prohibited by laws. Google addressed this problem in 2016 by introducing Federated Learning (FL), allowing users to train a model collaboratively by aggregating locally-computed updates while the dataset is kept in the local device. However, recent works have shown that the central server can infer sensitive information about the local dataset as it has access to updates of each client. Researchers are trying to provide an efficient solution for this problem, known as secure aggregation, in terms of the model's accuracy, communication, and computation cost for clients. Motivated by this problem, we will propose a scalable, highly efficient framework for clients that provides guaranteed privacy using additive secret sharing. 
Keywords: Deep Learning, Federated Learning, Secure Aggregation, Additive Secret Sharing 

MSc Thesis Committee:  
Internal Reader: Dr. Saeed Samet             
External Reader: Dr. Ning Zhang 
Advisor: Dr. Dima Alhadidi 
Chair: Dr. Luis Rueda

 MSc Thesis Defense Announcement 

Vector Institute in Artificial Intelligence, artificial intelligence approved topic logo



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