Protection of Neural Network Models from Privacy Breach Threats in Federated Learning Using Optimization Methods

This paper studies an approach to countering privacy breach threats in federated learning. The approach is based on optimization methods that transform the weights of local neural network models and create new weights for transmission to the joint gradient descent node, thereby preventing the interception of the local model’s weights by an attacker. The conducted experimental studies confirm the effectiveness of the developed approach.