Please use this identifier to cite or link to this item:
Title: Federated learning for image applications
Authors: Cao, Shuxin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Cao, S. (2021). Federated learning for image applications. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: With the development of artificial intelligence and deep learning, data privacy and security have become very important issues. For institutions or individuals to train the model together while ensuring data privacy, the concept of federated learning is proposed. In this project, we studied the aggregation algorithm FedAvg of federated learning and applied it to different models and image data. A FedAvg framework was built for further research. To summarize the emergence of the problem and the optimization direction, we also proposed the hidden dangers of privacy leakage in federated learning. We practice membership inference on federated learning and proposed a new attack algorithm SVDD-MI with higher accuracy compared with the previous attack work on a single model. Besides, we also give up ideas to some of the defense models. Lastly, we found that the gradient of the federated learning model has the problem of leaking the privacy of the original image and successfully reconstructed part of the training image by inverting gradients. Moreover, we propose some of the defense methods which perform a good result.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Cao Shuxin submit.pdf
  Restricted Access
4.53 MBAdobe PDFView/Open

Page view(s)

Updated on May 17, 2022


Updated on May 17, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.