Summary of Improving Accuracy and Convergence Of Federated Learning Edge Computing Methods For Generalized Der Forecasting Applications in Power Grid, by Vineet Jagadeesan Nair et al.
Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid
by Vineet Jagadeesan Nair, Lucas Pereira
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research aims to develop more accurate federated learning methods for forecasting distributed energy resources in modern power grids. The goal is to create faster and more efficient methods that can handle non-independent and identically distributed (non-IID) data. This will be achieved by leveraging recent advancements in hierarchical and iterative clustering, experimenting with different global models suitable for time-series data, and incorporating domain-specific knowledge from power systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train artificial intelligence models without sharing private data. It’s like a big team working together on a project. In this case, the goal is to make it better at predicting things like how much energy we’ll need in the future. This will help us create more accurate forecasts and make our power grids run smoothly. |
Keywords
» Artificial intelligence » Clustering » Federated learning » Time series
