Private Dataset Generation Using Privacy Preserving Collaborative Learning
This work addresses privacy concerns for edge-based machine learning applications, but it appears incremental as it builds on existing federated learning and privacy-preserving methods.
The authors tackled the problem of privacy and adversarial attacks in collaborative deep learning by introducing the FedCollabNN framework, which is computationally efficient and robust, with simulation results on the MNIST dataset indicating its effectiveness.
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.