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School of Computer Science Colloquium/PhD Seminar Announcement by Samaneh Mirirostami:"Blockchain-based Federated Learning "

Friday, November 12, 2021 - 11:00 to 12:30

SCHOOL OF COMPUTER SCIENCE – Colloquium Series – PhD Seminar Presentation 

The School of Computer Science at the University of Windsor is pleased to present…  

Colloquium Presentation #7 by PhD Candidate Samaneh Mirirostami 

Picture of Samaneh Mirirostami, School of Computer Science PhD candidate
Date: Friday November 12, 2021 
Time: 11:00am – 12:30pm 
Passcode: If interested in attending this event, contact the Graduate Secretary at with sufficient notice before the event to obtain the passcode.


Federated Learning has made an essential step towards enhancing the privacy of traditional model training. However, gaps in the conventional Federated Learning framework make it vulnerable. By following the data minimization principle, Federated Learning is dealing with a double-edged sword. Although it provides privacy by design, it makes data verification challenging as no one can see the other’s data. Therefore, participants may act dishonestly, increasing the risk of information leakage or performance degradation. It also lacks an incentive mechanism. Most recent studies leveraged blockchain technology to deal with privacy and security problems and address centralization and fairness issues. Blockchain-based Federated Learning is still in its infancy. It will continue to thrive and will be an active research area in the future because of its potentials for addressing security and privacy issues of distributed machine learning. 
Keywords: Federated learning, blockchain, privacy, incentive mechanism


Samaneh Miri is a Ph.D. candidate in computer science at the University of Windsor. She is currently working on the Federated Learning framework and its Privacy/Security issues. She earned her MSc in Information Technology Engineering in the field of Computer Networks from Shiraz University of Technology, Iran. Her research interests are around Big Data Analytics in healthcare, Privacy-Preserving Machine Learning, and Blockchain.  
Contact information:, 
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