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

 

Date: Tuesday September 20, 2022 

Time: 4:00-5:00 pm 

Meeting URL: https://us06web.zoom.us/j/83631808846?from=addon

Passcode: If interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca with sufficient notice before the event to obtain the passcode.

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 

Internal Reader: Dr. Saeed Samet             

External Reader: Dr. Ning Zhang               

Advisor: Dr. Dima Alhadidi 

 

 

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