分布式数据分类器学习协议
Protocols for Learning Classifiers on Distributed Data
摘要：
我们考虑的问题，学习分类器的标记数据，已分布在几个节点。我们的目标是找到一个单一的分类器，具有较小的近似误差，在所有的数据集，同时最大限度地减少节点之间的通信。这种设置模拟了处理大量分布式数据集时的真实通信瓶颈。我们提出了几个非常一般的基于采样的解决方案，以及一些双向协议，具有可证明的指数加速任何单向协议。我们专注于无噪声数据分布在两个或多个节点的核心问题。我们介绍的技术让人想起主动学习，但不是主动探测标签，而是节点主动相互通信，每个节点同时从另一个节点学习重要数据。
英文摘要：
We consider the problem of learning classifiers for labeled data that has been distributed across several nodes. Our goal is to find a single classifier, with small approximation error, across all datasets while minimizing the communication between nodes. This setting models real-world communication bottlenecks in the processing of massive distributed datasets. We present several very general sampling-based solutions as well as some two-way protocols which have a provable exponential speed-up over any one-way protocol. We focus on core problems for noiseless data distributed across two or more nodes. The techniques we introduce are reminiscent of active learning, but rather than actively probing labels, nodes actively communicate with each other, each node simultaneously learning the important data from another node.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
