Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Authors: Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, Julien Stainer
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also report on experimental evaluations of Krum. |
| Researcher Affiliation | Academia | Peva Blanchard EPFL, Switzerland peva.blanchard@epfl.ch El Mahdi El Mhamdi EPFL, Switzerland elmahdi.elmhamdi@epfl.ch Rachid Guerraoui EPFL, Switzerland rachid.guerraoui@epfl.ch Julien Stainer EPFL, Switzerland julien.stainer@epfl.ch |
| Pseudocode | No | The paper describes the Krum function in a prose paragraph but does not present it as a formally labeled pseudocode block or algorithm block formatted like code. |
| Open Source Code | No | The implementation is part of a larger distributed framework to run sgd in a reliable distributed fashion and will be released in the github repository of the distributed computing group at epfl, https://github.com/lpd-epfl. |
| Open Datasets | Yes | We consider the task of spam filtering (dataset spambase [19]). ... image classification (dataset MNIST). ... [19] M. Lichman. UCI machine learning repository, 2013. |
| Dataset Splits | No | We measure the error using cross-validation. The paper mentions cross-validation as an evaluation method, but it does not specify exact split percentages (e.g., 80/10/10), absolute sample counts for validation, or references to predefined validation splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory specifications, or cloud computing instance types used for running the experiments. It only refers to a 'distributed machine learning framework'. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1) needed to replicate the experiment. |
| Experiment Setup | Yes | The learning model is a multi-layer perceptron (MLP) with two hidden layers. ... Each (correct) worker estimates the gradient on a mini-batch of size 3. |