Update README.md

README.md

@@ -5,8 +5,7 @@ This is the codebase for our paper "Introducing Edge Intelligence to Smart Meter
 
 ## Overview
 
-Low-cost smart meters are ubiquitous in smart grids. Enabling resource-constraint smart meters to perform deep learning is quite challenging. Our end-edge-cloud framework reveals a new path for edge intelligence on smart meters. It improves 95.6% memory footprint, 94.8% training time, and 50% communication overhead, achieving comparable or even superior forecasting accuracy to the
-resource-unlimited methods. We provide code for established experimental and simulation platforms.
+Low-cost smart meters are ubiquitous in smart grids. Enabling resource-constraint smart meters to perform deep learning is quite challenging. Our end-edge-cloud framework reveals a new path for edge intelligence on smart meters. It improves 95.6% memory footprint, 94.8% training time, and 50% communication overhead, achieving comparable or even superior forecasting accuracy to that of conventional methods trained on high-capacity servers. We provide code for established experimental and simulation platforms.
 
 ![content](figures/framework.jpg)