Protocols for Learning Classifiers on Distributed Data
This addresses communication bottlenecks in processing massive distributed datasets, offering incremental improvements in protocol efficiency.
The paper tackles the problem of learning classifiers on distributed data with communication constraints, presenting sampling-based solutions and two-way protocols that achieve provable exponential speed-up over one-way protocols.
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.