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Improving Heterogeneous Model Reuse by Density Estimation

my enviroment:

  • Python 3
  • Linux

insatll package dependency:

pip install -r ./requirements.txt

project layout:

# code for toy example experiment
toy example/

# benchmark experiments
data/
    fashion_mnist/          # Fashion-MNIST datasets under multiparty settings
        A/
            ${party_name}/
                ${class_name}/
                    ${image_name}.png
        B/
        C/
        D/
        train/              # global train dataset
        test/               # global test dataset
        fashion_mnist.py    # code to load multiparty fashion datasets
fashion_mnist.ipynb         # reproduce all figures in the paper
fashion_mnist.conv/         # train classifiers on local dataset and train centralized baseline model
    data/                   # symbolic link to ../data/
    output/                 # symbolic link to ../output/
fashion_mnist.realnvp/      # train density estimators on locally
fashion_mnist.global/       # global model (Ours)
    deploy_global_model.py  # deploy the global model
    prepare_global_model.py # calibration the global model from raw local models (random initialized)
fashion_mnist.RKME/         # deploy the global model (RKME)
output/                     # training logs, model checkpoints

1. Toy Example

2. Benchmark Experiments on Fashion-MNIST

download tensorboard logs from github releases

prepare dataset

  1. download datasets from github releases
  2. unzip it to data/ dir

2.1 Ours

train classifiers on Fashion-MNIST:

cd fashion_mnist.conv
python3 prepare_conv.py # log dir: output/fashion_mnist/conv/log

train density estimators on Fashion-MNIST:

cd fashion_mnist.realnvp
python3 prepare_realnvp.py # log dir: output/fashion_mnist/realnvp/log

evaluate global model on global test set (10k images, 10 classes):

cd fashion_mnist.global
python3 deploy_global_model.py 
# zero-shot accuracy: output/fashion_mnist/global/deploy
# calibration log: output/fashion_mnist/global/calibration/log

train global model from raw model on global train set:

cd fashion_mnist.global
python3 prepare_global_model.py
# raw accuracy: output/fashion_mnist/global/raw
# log dir: output/fashion_mnist/global/raw/log

NOTE: structure of log directories:

.
├── A
│   ├── party_0
│   │   ├──  version_0
│   │   │... version_XX
│   └── party_1
├── B
│   ├── party_0
│   ├── party_1
│   └── party_2
├── C
│   ├── party_0
│   ├── party_1
│   └── party_2
└── D
    ├── party_0
    ├── party_1
    ├── party_2
    ├── party_3
    ├── party_4
    ├── party_5
    └── party_6

2.2 Centralized Baseline

cd fashion_mnist.conv
python3 prepare_baseline.py # log dir: output/fashion_mnist/conv/baseline/log

2.3 HMR (compared)

Wu, Xi Zhu, Song Liu, and Zhi Hua Zhou. 2019. “Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin.” 36th International Conference on Machine Learning, ICML 2019 2019-June: 11862–71.

see GitHub.
pre-run results - output/fashion_mnist.HMR/result.csv

2.4 RKME (compared)

X. Wu, W. Xu, S. Liu, and Z. Zhou. Model reuse with reduced kernel mean embedding specification. IEEE Transactions on Knowledge and Data Engineering, 35(01):699–710, jan 2023.

cd fashion_mnist_RKME

Kernel methods usually cannot work directly on the raw-pixel level or raw-document level due to the high input dimension. We exact features as the outputs from the penultimate layer of pre-trained ResNet-110.

python3 prepare_features.py # save features to: output/fashion_mnist.RKME/features.resnet101

fit reduced kernel mean embedding, find optimal betas and reduced points (M = 10).

python3 prepare_rkme.py # log dir: output/fashion_mnist.RKME/features.resnet101.RKME.M=10

deploy RKME on global test set.

python3 deploy_rkme.py # log dir: output/fashion_mnist.RKME/features.resnet101.RKME.M=10/deploy