Faster Communication with Proxskip: A Local Gradient Method

In this article, the authors present a new optimization method called Proxskip, which can accelerate communication between different devices in a distributed system. They demonstrate that Proxskip can significantly reduce the amount of data exchanged between devices while still achieving high accuracy in machine learning tasks.
To understand how Proxskip works, imagine you are trying to solve a complex math problem with a group of friends. You and your friends are working on different parts of the problem, but you need to share your work with each other to ensure everything adds up correctly. Proxskip is like a messenger who helps you communicate with your friends without having to send all the calculations you’ve done so far.
The authors show that by using Proxskip, you can significantly reduce the number of messages exchanged between devices while still achieving the same level of accuracy. This means that Proxskip can speed up the communication process and make it more efficient.
One interesting aspect of Proxskip is that it uses a technique called "local gradient steps" to improve its performance. Think of these local gradient steps as little jumps you take towards the correct answer while working on your math problem with your friends. By taking these jumps, you can quickly converge on the right solution without having to send all the calculations you’ve done so far.
Overall, Proxskip is a powerful optimization method that can help speed up communication between devices in a distributed system. Its ability to reduce the amount of data exchanged while still achieving high accuracy makes it an important tool for many applications, including machine learning and deep learning.