增强隐私保护的去中心化联邦学习

2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) Pub Date : 2022-07-18 DOI:10.1109/ICMEW56448.2022.9859507

Sheng-Po Tseng, Jan-Yue Lin, Wei-Chien Cheng, L. Yeh, Chih-Ya Shen

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引用次数: 1

摘要

我们提出了一个基于区块链的去中心化联邦学习(FL)框架。在传统的联邦学习中，需要第三方集中式服务器聚合参与上传的所有梯度，但这样的可信第三方可能并不总是存在。我们用去中心化的区块链解决了这个问题，并加密了神经网络模型参数和梯度。

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Decentralized Federated Learning with Enhanced Privacy Preservation

We present a decentralized federated learning (FL) framework based on blockchain. In traditional federated learning, it is necessary that a third-party centralized server aggregates all the gradients which participant in the upload, but such a trusted third-party may not always exist. We address this issue with the decentralized blockchain and encrypt the neural network model parameters and gradients.

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2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

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