Article ; Online: DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data.
2022 Volume 22, Issue 21
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 ... ...
| 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. |
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| MeSH term(s) | Blockchain ; Privacy ; Machine Learning |
| Language | English |
| Publishing date | 2022-10-28 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2052857-7 |
| ISSN | 1424-8220 ; 1424-8220 |
| ISSN (online) | 1424-8220 |
| ISSN | 1424-8220 |
| DOI | 10.3390/s22218263 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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