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Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting
March 5, 2024, 2:42 p.m. | Asif Iqbal, Prosanta Gope, Biplab Sikdar
cs.LG updates on arXiv.org arxiv.org
Abstract: Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. Smart meter data availability has led to the demand for sensor-based load forecasting. Conventional ML allows training a single global model using data from multiple smart meters requiring data transfer to a central server, raising concerns for network requirements, privacy, and security. We propose a split learning-based framework for load forecasting to alleviate this issue. We split a deep neural network model …
abstract arxiv availability collaborative cs.lg data demand energy energy management forecasting framework global grid infrastructure management multiple planning privacy sensor smart training transfer type
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