/SLR_public_serverclient

Primary LanguagePython

SLR_public_serverclient

SLR_server_client

This repository can be used to run the Server Client Split Learning Framework for benchmarking CIFAR-10 on n clients. FOllow the below steps to reproduce the results obtained of Setting-1 of the paper https://arxiv.org/abs/2303.10624

AWS Setup

Launch the below instance(s) in the same VPC and Subnet of a AWS region.

  • Instance 1: t2.xlarge (16 GB RAM)
  • Instance 2: t2.medium (4 GB RAM)

Initializing the instances

  • Each instance needs to initialized with the necessary libraries within a conda environment preferably.

  • Libraries needed

  • certifi 2022.12.7

  • charset-normalizer 2.1.1

  • filelock 3.9.0

  • idna 3.4

  • Jinja2 3.1.2

  • joblib 1.2.0

  • MarkupSafe 2.1.2

  • mpmath 1.2.1

  • networkx 3.0

  • numpy 1.24.2

  • pandas 2.0.0

  • Pillow 9.3.0

  • pip 23.0.1

  • PuLP 2.7.0

  • python-dateutil 2.8.2

  • pytz 2023.3

  • requests 2.28.1

  • scikit-learn 1.2.2

  • scipy 1.10.1

  • setuptools 67.6.1

  • six 1.16.0

  • sympy 1.11.1

  • threadpoolctl 3.1.0

  • torch 2.0.0+cpu

  • torchaudio 2.0.1+cpu

  • torchvision 0.15.1+cpu

  • typing_extensions 4.4.0

  • tzdata 2023.3

  • urllib3 1.26.13

  • wheel 0.40.0

Results (Setting-1 Cifar-10)

  • Datapoints Accuracy
  • "50","76.17"
  • "150","81.42"
  • "250","83.57"
  • "350","85"
  • "500","85.54"