A Hybrid Solution to improve Iteration Efficiency in the Distributed Learning
This addresses efficiency issues in distributed learning systems, though it appears incremental as it builds on existing fault-tolerant methods.
The paper tackles the problem of slow or failing nodes in distributed machine learning systems by proposing a hybrid approach that abandons results from slow machines each iteration, which dramatically reduces calculation time according to experiments.
Currently, many machine learning algorithms contain lots of iterations. When it comes to existing large-scale distributed systems, some slave nodes may break down or have lower efficiency. Therefore traditional machine learning algorithm may fail because of the instability of distributed system.We presents a hybrid approach which not only own a high fault-tolerant but also achieve a balance of performance and efficiency.For each iteration, the result of slow machines will be abandoned. Then, we discuss the relationship between accuracy and abandon rate. Next we debate the convergence speed of this process. Finally, our experiments demonstrate our idea can dramatically reduce calculation time and be used in many platforms.