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2012 AISTATS AISTATS 2012

Protocols for Learning Classifiers on Distributed Data

Abstract

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
📈 Trend Setter — Federated Learning
🧭 Keyword Pioneer — distributed classifier
🐣 Hot Topic Early Bird — active learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Hal Daume III , Jeff Phillips , Avishek Saha , Suresh Venkatasubramanian

Topics

Artificial Intelligence > Learning Paradigms > Federated Learning Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Active Learning Machine Learning > Optimization & Theory > Distributed Learning Machine Learning > Learning Paradigms > Federated Learning

Keywords

active learning communication efficiency sampling method distributed classifier two-way protocol distributed datum multi-node learning
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