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Secure decentralized peer-to-peer training of deep neural networks based on distributed ledger technology

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Abstract

The accuracy and performance of deep neural network models become important issues as the applications of deep learning increase. For example, the navigation system of autonomous self-driving vehicles requires very accurate deep learning models. If a self-driving car fails to detect a pedestrian in bad weather, the result can be devastating. If we can increase the model accuracy by increasing the training data, the probability of avoiding such scenarios increases significantly. However, the problem of privacy for consumers and lack of enthusiasm for sharing their personal data, e.g., the recordings of their self-driving car, is an obstacle for using this valuable data. In Blockchain technology, many entities which cannot trust each other in normal conditions can join together to achieve a mutual goal. In this paper, a secure decentralized peer-to-peer framework for training the deep neural network models based on the distributed ledger technology in Blockchain ecosystem is proposed. The proposed framework anonymizes the identity of data providers and therefore can be used as an incentive for consumers to share their private data for training deep learning models. The proposed framework uses the Stellar Blockchain infrastructure for secure decentralized training of the deep models. A deep learning coin is proposed for Blockchain compensation.

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Authors and Affiliations

  1. Department of Computer Engineering, Khatam University, North Shiraz Street, Tehran, Iran

    Amin Fadaeddini & Babak Majidi

  2. Department of Computer Engineering, Shahid Beheshti University, Tehran, Iran

    Mohammad Eshghi

Authors
  1. Amin Fadaeddini
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  2. Babak Majidi
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  3. Mohammad Eshghi
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Correspondence to Babak Majidi.

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Fadaeddini, A., Majidi, B. & Eshghi, M. Secure decentralized peer-to-peer training of deep neural networks based on distributed ledger technology. J Supercomput 76, 10354–10368 (2020). https://doi.org/10.1007/s11227-020-03251-9

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  • DOI: https://doi.org/10.1007/s11227-020-03251-9

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