Summary of Exploring Lightweight Federated Learning For Distributed Load Forecasting, by Abhishek Duttagupta et al.
Exploring Lightweight Federated Learning for Distributed Load Forecasting
by Abhishek Duttagupta, Jin Zhao, Shanker Shreejith
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 paper explores Federated Learning (FL) for analyzing smart energy meter data while ensuring privacy and achieving comparable accuracy to state-of-the-art methods for load forecasting. A lightweight fully connected deep neural network is used, demonstrating comparable forecasting accuracy at both individual meter sources and the aggregator level. The FL framework reduces energy and resource consumption by utilizing lightweight models, making it suitable for deployment across resource-constrained smart meter systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses Federated Learning to analyze smart energy meter data in a private and accurate way. It shows that a simple deep learning model can be used to forecast electricity usage, which is better than previous methods. This approach uses less energy and is good for using on small devices like smart meters. |
Keywords
* Artificial intelligence * Deep learning * Federated learning * Neural network
