【Author】
Hu, Shili; Li, Jiangfeng; Zhang, Chenxi; Zhao, Qinpei; Ye, Wei
【Source】2021 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN (BLOCKCHAIN 2021)
【Abstract】Nowadays, privacy-preserving artificial intelligence is gaining traction, with the goal of learning multiple models based on private data without leaking any personal information. Since the existing multi-party computation methods and other encryption-based methods have their flaws, we developed our own blockchain-based edge computing framework to achieve the decentralization and enhance the efficiency. Our framework enables a trustful, simplified and asynchronous federated learning in IoT and provides a convenient and secret classification service. Extensive evaluations on efficiency are provided, confirming the performance of our solutions.
【Keywords】blockchain; federated learning; edge computing; privacy
【标题】用于保护隐私的联邦学习的基于区块链的边缘计算框架
【摘要】如今，保护隐私的人工智能越来越受到关注，其目标是在不泄露任何个人信息的情况下基于私人数据学习多个模型。由于现有的多方计算方法和其他基于加密的方法存在缺陷，我们开发了自己的基于区块链的边缘计算框架，以实现去中心化并提高效率。我们的框架在物联网中实现了可信、简化和异步的联邦学习，并提供了方便和秘密的分类服务。提供了对效率的广泛评估，确认了我们解决方案的性能。
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