Abstract
In the smart grid, the underlying data often contains a large amount of private information and cannot be shared, which further limits the effect of large-scale data analysis and utilization. Federated learning, as a decentralized learning strategy with a privacy protection function, can learn a wider range of knowledge through model aggregation without touching local data and is suitable for smart grid scenarios. Considering the limited computing and storage capabilities of the underlying terminal of the grid system, this paper proposes a three-tier federation architecture of the end-side-cloud, using model segmentation technology to only retain the optimized feature extraction model on the local client. The back-end models that need to be repeatedly iteratively trained are allocated to edge servers and interact with the cloud to learn collaboratively, thereby reducing system overhead. We conducted extensive experiments to verify the superiority of the federated model compared to the independent training model and further analyzed the feasibility of edge computing.
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Ji, X., Zhang, H., Chu, J., Dong, L., Yang, C. (2022). New Federation Algorithm Based on End-Edge-Cloud Architecture for Smart Grid. In: Cao, C., Zhang, Y., Hong, Y., Wang, D. (eds) Frontiers in Cyber Security. FCS 2021. Communications in Computer and Information Science, vol 1558. Springer, Singapore. https://doi.org/10.1007/978-981-19-0523-0_8
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DOI: https://doi.org/10.1007/978-981-19-0523-0_8
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