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Enhancing the blockchain interoperability through federated learning with directed acyclic graph

IET blockchain, 2023-12, Vol.3 (4), p.238-248 [Peer Reviewed Journal]

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Title:
Enhancing the blockchain interoperability through federated learning with directed acyclic graph
Author: Xia, Feng ; Kaiye, Li ; Songze, Wu ; yan, Xin
Subjects: blockchain applications and digital technology ; blockchains ; data privacy ; data protection
Is Part Of: IET blockchain, 2023-12, Vol.3 (4), p.238-248
Description: Abstract The use of federated learning to achieve blockchain interoperability has become a hot topic in research, because it enables data exchange without revealing any private information. However, the previous work, such as ScaleSFL (Asia‐CCS, 2022), that has implemented federated learning for blockchain interoperability, the throughput of the framework still cannot support the practical applications. Therefore, a federated learning framework based on Directed Acyclic Graph (DAG) is proposed which utilizes sharding mechanism to enhance the blockchain interoperability. By constructing a weighted context graph based on data correlation, reasonable sharding of the dataset is achieved, thereby improving the efficiency of blockchain interoperability. The experimental results show that the federated framework reduces global computation in federated learning by 30% compared with other schemes, while increasing blockchain throughput by nearly 40%.
Publisher: Wiley
Language: English
Identifier: ISSN: 2634-1573
EISSN: 2634-1573
DOI: 10.1049/blc2.12033
Source: DOAJ Directory of Open Access Journals

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