一句话：提出首个异构感知的去中心化训练协议 Hop，利用迭代间隔设计队列同步与跳步机制，缓解慢节点影响并加速训练。
在机器学习中，去中心化训练虽可能优于中心化训练，但与后者一样在异构环境下会因慢节点而性能下降。现有异构适应研究多面向中心化算法，去中心化场景探索不足。本文提出 Hop，首个异构感知的去中心化训练协议。作者识别出去中心化训练特有的“迭代间隔”特征，并据此设计基于队列的同步机制，在去中心化设置中高效实现备份工作节点和有界陈旧度；针对确定性减速，进一步提出跳过迭代以减弱慢节点影响。论文在 TensorFlow 上实现 Hop 原型，并在 CNN 与 SVM 上评估。实验表明，在异构设置下 Hop 相比标准去中心化训练取得显著加速。
Recent work has shown that decentralized algorithms can deliver superior performance over centralized ones in the context of machine learning. The two approaches, with the main difference residing in their distinct communication patterns, are both susceptible to performance degradation in heterogeneous environments. Although vigorous efforts have been devoted to supporting centralized algorithms against heterogeneity, little has been explored in decentralized algorithms regarding this problem. This paper proposes Hop, the first heterogeneity-aware decentralized training protocol. Based on a unique characteristic of decentralized training that we have identified, the iteration gap, we propose a queue-based synchronization mechanism that can efficiently implement backup workers and bounded staleness in the decentralized setting. To cope with deterministic slowdown, we propose skipping iterations so that the effect of slower workers is further mitigated. We build a prototype implementation of Hop on TensorFlow. The experiment results on CNN and SVM show significant speedup over standard decentralized training in heterogeneous settings.