分布式编码机器学习的激励机制设计
Incentive Mechanism Design for Distributed Coded Machine Learning
摘要：
分布式机器学习平台需要招募多个异构工作节点来同时完成计算。因此，由于分散的工作者，整体性能可能会下降。通过在计算中引入冗余，编码的机器学习可以通过通过完成计算的第一个$k$（在总$n$中）工作者恢复最终计算结果来有效地提高运行时性能。虽然现有的研究集中在设计有效的编码方案，设计适当的激励措施，以鼓励工人参与的问题仍然是探讨不足。本文研究了平台的最优激励机制，激励适当的工人参与编码机器学习，尽管异构工人的计算性能和成本的不完全信息。这项工作的一个关键贡献是将工人的多维异质性总结为一维度量，该度量指导平台在具有线性计算复杂度的不完全信息下有效地选择工人。此外，我们还证明了如果采用广泛采用的MDS（Maximum Distance Separable）码进行数据编码，则最佳恢复门限$k$与译码器数目$n$成线性关系。我们还表明，该平台的成本增加，由于不完全信息消失时，工人数量足够大，但它不会单调减少工人数量。
英文摘要：
A distributed machine learning platform needs to recruit many heterogeneous worker nodes to finish computation simultaneously. As a result, the overall performance may be degraded due to straggling workers. By introducing redundancy into computation, coded machine learning can effectively improve the runtime performance by recovering the final computation result through the first $k$ (out of the total $n$) workers who finish computation. While existing studies focus on designing efficient coding schemes, the issue of designing proper incentives to encourage worker participation is still under-explored. This paper studies the platform's optimal incentive mechanism for motivating proper workers' participation in coded machine learning, despite the incomplete information about heterogeneous workers' computation performances and costs. A key contribution of this work is to summarize workers' multi-dimensional heterogeneity as a one-dimensional metric, which guides the platform's efficient selection of workers under incomplete information with a linear computation complexity. Moreover, we prove that the optimal recovery threshold $k$ is linearly proportional to the participator number $n$ if we use the widely adopted MDS (Maximum Distance Separable) codes for data encoding. We also show that the platform's increased cost due to incomplete information disappears when worker number is sufficiently large, but it does not monotonically decrease in worker number.
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