title: ÐÐ°ÑÐ¸ÑÐ° Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¾Ñ ÑÐ³ÑÐ¾Ð· Ð½Ð°ÑÑÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð² ÑÐµÑÑÑ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸ÑÑÐ°
titleType: Subtitle
title: Protecting neural network models from privacy violation threats in federated learning networks
Creators: name: ÐÐµÐ·Ð±Ð¾ÑÐ¾Ð´Ð¾Ð², ÐÐ°Ð²ÐµÐ»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð¼ÐµÑÐ¾Ð´Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ ÑÐ¿ÑÑÐº
federated learning
neural network models
optimization methods
gradient descent

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description: The topic of the graduate qualification work is Â«Protecting neural network models from privacy violation threats in federated learning networksÂ». The purpose of the study is countering threats of privacy violation in federated learning networks. The subject of the study is methods on privacy protection of neural network models and gradient descent optimization. The research set the following goals: 1. Investigate the privacy issues of neural network models in federated learning networks. 2. Analyze the existing methods to ensure the privacy of neural network models in federated learning networks. 3. Evaluate the feasibility of gradient descent optimization techniques to ensure the privacy of neural network models. 4. Perform a software implementation of the proposed method. 5. Evaluate the robustness of the method in terms of privacy and determine the optimal parameter values. During the work the possibility of manipulating gradient descent to protect neural network models of federated learning nodes was investigated. method was proposed to ensure the privacy of neural network models of federated learning participants. It was concluded that further research on security issues in federated learning networks is needed. The results could be used as a basis for creating federated learning networks with increased privacy of neural network models.