Computer Engineering Department, Gachon University, Seongnam 1342, Korea.
Sensors (Basel). 2022 Oct 28;22(21):8263. doi: 10.3390/s22218263.
Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.
联邦学习是一种隐私保护、协作式机器学习。在联邦学习过程中，它不是共享原始数据，而是通过多个用户以去中心化的方式协作交换模型参数并对其进行聚合。在这项研究中，我们设计并实现了一个分层区块链系统，该系统使用公共区块链来进行没有可信管理员的联邦学习过程。这可以防止模型中毒攻击，并提供全局模型的安全更新。我们进行了全面的实证研究，以在我们的测试平台上描述联邦学习的性能，并确定潜在的性能瓶颈，从而更好地了解系统。
