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- W4318187106 abstract "In order to realize the data sharing of isolated power system and support the intelligent decision-making of power grid, a data sharing scheme based on blockchain and federated learning has emerged. However, the lack of computing attributes in blockchain makes it difficult to guarantee the trust of federated learning process. And due to the lack of federated learning task scheduling algorithm, data sharing based on federated learning in power grid is inefficient. To solve above problems, this paper proposes a state channel-based federated learning architecture which establishes a soft computing sandbox through the state channel technology to realize the supervision of the federated learning training process. To match federated learning tasks with heterogeneous device nodes in power grid, we propose a task scheduling algorithm. After testing, it is found that the algorithm proposed in this paper has outstanding performance in many indicators. The average task response delay is shortened by 38.96 %. The response latency of tasks deployed by high priority users is reduced by at least 27.52 %; Under high load conditions, the system blocking time can be delayed by 10.47%." @default.
- W4318187106 created "2023-01-27" @default.
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- W4318187106 date "2022-12-16" @default.
- W4318187106 modified "2023-09-22" @default.
- W4318187106 title "Federated Learning Task Scheduling Algorithm For Trusted Sharing Of Power Data" @default.
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- W4318187106 doi "https://doi.org/10.1109/imcec55388.2022.10019914" @default.
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