Before revision:  In this paper, we propose  to eliminate stragglers by adapting each worker's training load to its processing capability; that is , slower workers receive a smaller batch of data to process. Following this idea, we develop a new synchronization scheme called LB-BSP (Load-balanced BSP).
After revision:  In this paper, we propose to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at iteration boundaries, being non-intrusive to intra-iteration execution. We develop LB-BSP (Load-balanced BSP).