| Autor: |
LIU Tianrui, WANG Lianhai, WANG Qizheng, XU Shujiang, ZHANG Shuhui, WANG Yingxiaochun |
| Jazyk: |
English<br />Chinese |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
网络与信息安全学报, Vol 10, Pp 130-142 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2096-109X |
| DOI: |
10.11959/j.issn.2096-109x.2024045 |
| Popis: |
The computational power network was aimed to deeply integrate arithmetic resources and network resources, achieving efficient utilization of massive data and heterogeneous resources in ubiquitous discrete deployment. To cope with the complicated multicenter computing collaboration and requirements for data privacy and security in computational power networks, federated learning was recognized for its inherent advantages in privacy protection and edge arithmetic resource utilization. However, federated learning in the computational power network environment faced certain difficulties due to the highly heterogeneous and widely distributed arithmetic edge servers. On one hand, the heterogeneity of data among the massive edge servers in the computational power network caused the Non-IID problem, leading to the deviation of the local model update from the global optimum in federated learning. On the other hand, the generation of low-quality local models could significantly affect the training effect due to the differences in data quality among different edge servers. To solve the above problems, a blockchain-based adaptive federated learning framework AWFL-BC (adaptive weight federated learning-blockchain) was proposed. Initially, the data distribution distances of different edge servers were calculated through smart contracts to generate a similarity matrix that guided aggregation. Concurrently, an adaptive weight aggregation algorithm was designed to alleviate the decrease in model performance and stability caused by differences in data quality, thereby improving the accuracy of the model and accelerating model convergence. Finally, the integration of blockchain technology strengthened the security mechanism, effectively preventing poisoning attacks and inference attacks. Comprehensive experiments on three public standard datasets show that AWFL-BC achieves higher model accuracy and faster model convergence compared to state-of-the-art methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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