Large-Scale Distributed Learning via Private On-Device LSH
Authors: Tahseen Rabbani, Marco Bornstein, Furong Huang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we (1) gauge the sensitivity of PGHash and (2) analyze the performance of PGHash and our own DWTA variant (PGHash-D) in training large-scale recommender networks. |
| Researcher Affiliation | Academia | Tahseen Rabbani Department of Computer Science University of Maryland trabbani@umd.edu Marco Bornstein Department of Computer Science University of Maryland marcob@umd.edu Furong Huang Department of Computer Science University of Maryland furongh@umd.edu |
| Pseudocode | Yes | Algorithm 1 Distributed PGHash |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We use three extreme multi-label datasets for training recommender networks: Delicious-200K, Amazon-670K, and Wiki LSHTC-325K. These datasets come from the Extreme Classification Repository [4]. and the citation [4] is "Kush Bhatia, Kunal Dahiya, Himanshu Jain, Anshul Mittal, Yashoteja Prabhu, and Manik Varma. The extreme classification repository: Multi-label datasets and code. URL http://manikvarma. org/downloads/XC/XMLRepository. html, 2016." |
| Dataset Splits | No | The paper mentions 'test accuracy' and 'test sets' but does not provide explicit training, validation, and test dataset splits or specific methodologies for partitioning the data into these subsets. |
| Hardware Specification | Yes | These experiments are run on a cloud cluster using Intel Xeon Silver 4216 processors with 128GB of total memory. |
| Software Dependencies | No | Finally, we train our neural network using Tensor Flow. |
| Experiment Setup | Yes | Table 1: Hyper-parameters for Federated Experiments (PGHash and Federated SLIDE). Dataset Algorithm Hash Type LR Batch Size Steps per LSH k c Tables CR Delicious-200K PGHash PGHash 1e-4 128 1 8 8 50 1 |