Summary of Privacy-preserving Collaborative Split Learning Framework For Smart Grid Load Forecasting, by Asif Iqbal et al.
Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting
by Asif Iqbal, Prosanta Gope, Biplab Sikdar
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for load forecasting in smart grids uses a split learning-based approach to alleviate concerns about network requirements, privacy, and security. By splitting a deep neural network into two parts, one for each grid station (GS) responsible for an entire neighborhood’s smart meters and the other for the service provider (SP), client smart meters can use their respective GSs’ model splits for forward pass and only share their activations with the GS. This approach allows each GS to train a personalized model split for their respective neighborhoods, while the SP can train a single global or personalized model for each GS. The proposed models match or exceed the performance of a centrally trained model and generalize well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Smart meters in your neighborhood are very important for managing energy and planning infrastructure. Right now, it’s hard to make accurate predictions about how much electricity people will use because we don’t have enough data. A team of researchers came up with a new way to do load forecasting using smart meter data that doesn’t require sharing sensitive information. They split the problem into smaller parts, so each neighborhood has its own model and only shares information locally. This makes it safer and more efficient. |
Keywords
* Artificial intelligence * Neural network
