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- W3021042056 abstract "With increasing usage of deep learning algorithms in many application, new research questions related to privacy and adversarial attacks are emerging. However, the deep learning algorithm improvement needs more and more data to be shared within research community. Methodologies like federated learning, differential privacy, additive secret sharing provides a way to train machine learning models on edge without moving the data from the edge. However, it is very computationally intensive and prone to adversarial attacks. Therefore, this work introduces a privacy preserving FedCollabNN framework for training machine learning models at edge, which is computationally efficient and robust against adversarial attacks. The simulation results using MNIST dataset indicates the effectiveness of the framework." @default.
- W3021042056 created "2020-05-13" @default.
- W3021042056 creator A5013704954 @default.
- W3021042056 date "2020-04-28" @default.
- W3021042056 modified "2023-09-27" @default.
- W3021042056 title "Private Dataset Generation Using Privacy Preserving Collaborative Learning." @default.
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