Summary of Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers, by Shourya Bose et al.
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
by Shourya Bose, Yu Zhang, Kibaek Kim
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 Personalization Layers for Federated Learning (PL-FL) framework improves the quality of short-term load forecasting models by incorporating personalization layers into federated learning. By leveraging heterogeneous client data, PL-FL outperforms traditional federated learning and local training methods while reducing communication bandwidth requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Smart meters collect energy consumption data for better short-term load forecasting. To protect privacy concerns, a new approach called Personalization Layers for Federated Learning (PL-FL) is proposed to train models without sharing private data. PL-FL helps to create more accurate forecasts than previous methods while using less data transmission. |
Keywords
* Artificial intelligence * Federated learning
