Before revision:  Our algorithm is  practical: it improves upon the performance of  prior methods when training deep neural networks, it is relatively lightweight, and  is the first method to withstand two recently-proposed Byzantine attacks.
After revision:  Our algorithm is practical: it improves upon the performance of prior methods when training deep neural networks, it is relatively lightweight, and it is the first method to withstand two recently-proposed Byzantine attacks.