张泽辉, 管聪, 高航, 等. 面向船联网的高效隐私保护联邦学习方法[J]. 中国舰船研究, 2022, 17(6): 48–58. doi: 10.19693/j.issn.1673-3185.02594
引用本文: 张泽辉, 管聪, 高航, 等. 面向船联网的高效隐私保护联邦学习方法[J]. 中国舰船研究, 2022, 17(6): 48–58. doi: 10.19693/j.issn.1673-3185.02594
shu
ZHANG Z H, GUAN C, GAO H, et al. Efficient privacy-preserving federated learning method for Internet of Ships[J]. Chinese Journal of Ship Research, 2022, 17(6): 48–58. doi: 10.19693/j.issn.1673-3185.02594
Citation: ZHANG Z H, GUAN C, GAO H, et al. Efficient privacy-preserving federated learning method for Internet of Ships[J]. Chinese Journal of Ship Research, 2022, 17(6): 48–58. doi: 10.19693/j.issn.1673-3185.02594
shu

面向船联网的高效隐私保护联邦学习方法

Efficient privacy-preserving federated learning method for Internet of Ships

    摘要
  • 摘要:
      目的  人工智能技术已经成为提升船舶航行安全水平、降低航运企业运营成本的重要手段。为打破不同船舶公司之间的数据壁垒，进一步提升船舶智能化水平，提出一种面向船联网的高效隐私保护联邦学习方法。
      方法  采用联邦学习技术以实现多个船舶参与方协同训练一个深度学习模型，并且使用同态加密方案以保护参与方的本地数据信息。考虑船联网场景，引入稀疏化技术对船舶参与方上传的模型参数进行压缩，从而降低参与方上传的数据量。
      结果  理论分析和实验结果表明，所提出的高效联邦故障诊断方法能够有效地降低密码学计算和数据通信的资源消耗，同时保护船舶参与方的本地数据信息。
      结论  该学习方法能够为船舶智能化研究提供参考。

     

    Abstract:
      Objectives  Artificial intelligent technologies have become an important approach to improving the safety of shipping and reducing the operating costs of shipping companies. In order to further improve the level of ship intelligence and break down the data barriers between different shipping companies, an efficient privacy-preserving federated learning method (EPFL) is proposed in this paper.
      Methods  Federated learning is adopted to organize multiple ship participants to collaboratively train a global fault diagnosis model, and cryptography technologies are used to protect their local data information. Considering Internet of Ships (IoS) scenarios, this paper introduces sparsification technology to compress the model parameters uploaded by shipping participants and reduce their number.
      Results  Theoretical analysis and the experimental results show that the proposed EPFL method can effectively reduce the resource consumption of cryptographic computation and data communication while protecting the local data information of ship participants.
      Conclusions  The proposed EPFL method can provide references for the establishment of intelligent ship systems.

     

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