# Data Driven Resource Allocation for Distributed Learning

arXiv: https://arxiv.org/abs/1512.04848 · HTML: https://mlsanity.com/paper/1512.04848/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2015.

## Built on by (most cited first, 3 of 3)

- [Learning-Based Heavy Hitters and Flow Frequency Estimation in Streams](https://mlsanity.com/paper/2406.16270/) (2024)
- [Conservative classifiers do consistently well with improving agents: characterizing statistical and online learning](https://mlsanity.com/paper/2506.05252/) (2025)
- [Federated Learning with Superquantile Aggregation for Heterogeneous Data](https://mlsanity.com/paper/2112.09429/) (2021)

## Abstract (arXiv)

In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou 1993), we propose data dependent dispatching that takes advantage of such structure. We present an in-depth analysis of this model, providing new algorithms with provable worst-case guarantees, analysis proving existing scalable heuristics perform well in natural non worst-case conditions, and techniques for extending a dispatching rule from a small sample to the entire distribution. We overcome novel technical challenges to satisfy important conditions for accurate distributed learning, including fault tolerance and balancedness. We empirically compare our approach with baselines based on random partitioning, balanced partition trees, and locality sensitive hashing, showing that we achieve significantly higher accuracy on both synthetic and real world image and advertising datasets. We also demonstrate that our technique strongly scales with the available computing power.