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Research on Deep Learning Based on Decentralized Differential Privacy Protection

Published: 31 July 2024 Publication History

Abstract

Most existing decentralized learning algorithms focus on improving the convergence speed and communication efficiency of models, without paying too much attention to the issue of participant privacy leakage. Differential privacy is currently one of the mainstream privacy protection technologies in the field of deep learning, widely used to protect the data privacy and security of participants. This article proposes a decentralized deep learning algorithm (PriDCNN) for differential privacy protection. The PriDCNN algorithm is based on a decentralized network topology, which avoids the problem of communication traffic congestion in the central node. Nodes cooperate with each other to train deep learning models, resulting in improved communication efficiency. For users with smaller local datasets, this approach can improve the performance of the local model. At the same time, the algorithm protects the training data of participants from being leaked by adding noise. The process of adding noise to PriDCNN is only used as a preprocessing method for the model and is not limited by the number of iteration rounds, which is beneficial for model optimization.

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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