DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data

【Author】 Lee, Jungjae; Kim, Wooseong

【Source】SENSORS

【影响因子】3.847

【Abstract】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.

【Keywords】blockchain; federated learning; smart contract; model-poisoning attack

【发表时间】2022 NOV

【收录时间】2022-11-25

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-联邦学习