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Research on Federated Learning and Its Security Issues for Load Forecasting

Published: 14 October 2021 Publication History

Abstract

The safe and effective management and utilization of the load data of electricity has become one of the important issues for power supply and distribution departments as electricity is an important part of industry 4.0. Accurate forecasting of power load is of great significance for the safety and stability of power grid dispatching and economical operation. However, many of the current power data sets have serious problems of data island; furthermore, the centralized storage of large amounts of data may cause privacy leakage of the original data owners and faces regulations of security supervision. Therefore, federated learning is introduced to address these issues. Nevertheless, this approach is not sufficient to provide adequate data privacy protection. The present research proposes a federated learning model based on improved differential privacy algorithm. The model uses multi-scale Laplacian algorithm to analyze data distribution and generate noises in accordance with data patterns. Moreover, the parameters of the model are protected by attribute-based access control (ABAC). The simulation results show that the model proposed by the present research makes accurate forecasting and the improved differential privacy algorithm has less influence on the model's accuracy; the model also shows a good resistance to attacks, which ensures the security of data while having a high precision.

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Cited By

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  • (2023)Personalized Federated Learning for Heterogeneous Residential Load ForecastingBig Data Mining and Analytics10.26599/BDMA.2022.90200436:4(421-432)Online publication date: Dec-2023
  • (2022)A federated learning model based on filtering strategyWorld Wide Web10.1007/s11280-022-01074-726:3(1031-1053)Online publication date: 24-Jun-2022

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cover image ACM Other conferences
ICCMS '21: Proceedings of the 13th International Conference on Computer Modeling and Simulation
June 2021
276 pages
ISBN:9781450389792
DOI:10.1145/3474963
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2021

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Author Tags

  1. Data Security
  2. Federated Learning
  3. Industry 4.0
  4. Load Forecasting

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Cited By

View all
  • (2023)Personalized Federated Learning for Heterogeneous Residential Load ForecastingBig Data Mining and Analytics10.26599/BDMA.2022.90200436:4(421-432)Online publication date: Dec-2023
  • (2022)A federated learning model based on filtering strategyWorld Wide Web10.1007/s11280-022-01074-726:3(1031-1053)Online publication date: 24-Jun-2022

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