Secure Distributed Training at Scale

This repository contains the implementation of experiments from the paper

"Secure Distributed Training at Scale"

Eduard Gorbunov*, Alexander Borzunov*, Michael Diskin, Max Ryabinin

[PDF] arxiv.org

Overview

The code is organized as follows:

• ./resnet is a setup for training ResNet18 on CIFAR-10 with simulated byzantine attackers
• ./albert runs distributed training of ALBERT-large with byzantine attacks using cloud instances

ResNet18

This setup uses torch.distributed for parallelism.

Requirements

• Python >= 3.7 (we recommend Anaconda python 3.8)
• Dependencies: pip install jupyter torch>=1.6.0 torchvision>=0.7.0 tensorboard
• A machine with at least 16GB RAM and either a GPU with >24GB memory or 3 GPUs with at least 10GB memory each.
• We tested the code on Ubuntu Server 18.04, it should work with all major linux distros. For Windows, we recommend using Docker (e.g. via Kitematic).

Running experiments: please open ./resnet/RunExperiments.ipynb and follow the instructions in that notebook. The learning curves will be available in Tensorboard logs: tensorboard --logdir btard/resnet.

ALBERT

This setup spawns distributed nodes that collectively train ALBERT-large on wikitext103. It uses a version of the hivemind library modified so that some peers may be programmed to become Byzantine and perform various types of attacks on the training process.

Requirements

• The experiments are optimized for 16 instances each with a single T4 GPU.

  • For your convenience, we provide a cost-optimized AWS starter notebook that can run experiments (see below)
  • While it can be simulated with a single node, doing so will require additional tuning depending on the number and type of GPUs available.

• If running manually, please install the core library on each machine:

  • The code requires python >= 3.7 (we recommend Anaconda python 3.8)
  • Install the library: cd ./albert/hivemind/ && pip install -e .
  • If successful, it should become available as import hivemind

Running experiments: For your convenience, we provide a unified script that runs a distributed ALBERT experiment in the AWS cloud ./albert/experiments/RunExperiments.ipynb using preemptible T4 instances. The learning curves will be posted to the Wandb project specified during the notebook setup.

Expected cloud costs: a training experiment with 16 hosts takes up approximately $60 per day for g4dn.xlarge and $90 per day for g4dn.2xlarge instances. One can expect a full training experiment to converge in ≈3 days. Once the model is trained, one can restart training from intermediate checkpoints and simulate attacks. One attack episode takes up 4-5 hours depending on cloud availability.