Code for paper 'Squeezing More Utility via Adaptive Clipping on Deferentially Private Gradients in Federated Meta-Learning' ACSAC 2022. Our differentially private algorithm is based on the MAML implementation at 'https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch' [1]

[1] Antreas Antoniou et al. 2019. How to train your MAML. In International Conference on Learning Representations (ICLR 19)

• Hardware: a server equipped with a 3.3 GHz Intel Core i9-9820X CPU, three GeForce RTX 2080 Ti GPUs (a different CPU/GPU is ok if it support the Pytorch deep learning framewotk)
• Operating system: Ubuntu 18.04.3 LTS (a different version of Ubuntu is ok if it support the Pytorch deep learning framewotk)
• Deep learning framework: Pytorch 1.4.0
• Language: Python 3.6.10

See "Prepare the environment" to set up Pytorch 1.4.0 and Python 3.6.10. Please read step by step to get ready for running the code.

If you have already set up your GPU for deep learning, please ignore this step and go to "Prepare the environment".

If you have never setup your GPU machine for deep learning, a guide is available at Deep Learning GPU Installation. This guide includes three steps: NVIDIA Driver installation, CUDA installation, and CUDNN installation.

1. Implement annoconda following the instruction in https://www.anaconda.com/. Anaconda will help to manage the learning environement.
2. Create your env using the environment.yml file included in the repository. All the required libraries (including pytorch and python) will be implemented. The default environment name is myenv as specified in the yml file. If you would like to use another name, edit the first line of the environment.yml file.

•  conda env create --file environment.yml
  

3. activate your environment.

•  conda activate envname
  

Download the repository to your local machine

•  git clone [email protected]:ning-wang1/DPFedMeta.git
  

The downloaded folder is named as 'DPFedMeta', I'll call this folder by the project folder in the following. The folder structure is:

├── checkpoint
├── experiment_config
├── experiment_scripts
├── result
├── tfcode
│   ├── __init__.py
│   └── rdp_accountant.py
├── utils
│   ├── __init__py
│   ├── dataset_tools.py
│   ├── dp_utils.py
│   ├── mnist.py
│   ├── parser_utils.py
│   └── storage.py
├── .gitignore
├── base_experiment.py
├── compute_dp_sgd_privacy.py
├── data.py
├── data_tasks_split.py
├── df_few_shot_learning_system.py
├── df_few_shot_learning_system_example_level_single.py
├── DPAGR.py
├── DPAGRLR.py
├── environment.yml
├── experiment_builder.py
├── inner_loop_optimizer.py
├── meta_neural_network_architectures.py
├── README.md
└── vgg.py

The main functions are DPAGR.py and DPAGRLR.py

We use three datasets, including Omniglot [1], CIFAR-FS [2], and Mini-ImageNet [3]. For convenience, the link to download the data are listed:

omniglot dataset

CIFAR-FS dataset

mini-ImageNet dataset

1. Create a folder in your project folder and name it as 'datasets'
2. Download the datasets from the above links
3. Rename each downloaded dataset in case its default name is different from that used in the code.
   1. name the ominiglot dataset as 'omniglot_dataset'
   2. name the CIFAR-FS dataset as 'cifar_100'
   3. name the mini-ImageNet dataset as 'mini_imagenet_full_size' Be sure to coordnate your reference path and the dataset path.

[1] Brenden M Lake et al. 2015. Human-level concept learning through probabilistic program induction. Science 350, 6266 (2015), 1332–1338

[2] Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi. 2019. Meta-learning with differentiable closed-form solvers. In International Conference on Learning Representations (ICLR 19)

[3] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al . 2016. Matching networks for one shot learning. In Advances in neural information processing systems (NeurIPS 16). 3630–3638

1. go to the folder experiment_scripts: cd experiment_scripts
2. Training with DPAGR algorithm which protects user level privacy

for Omniglot dataset:

•   bash omniglot_DPAGR.sh
  

for mini_ImageNet dataset:

•   bash mini-imagenet_DPAGR.sh 
  

for CIFAR-FS dataset:

•   bash cifar-fs_DPAGR.sh
  

3. Training with DPAGRLR algorithm which protects both user level privacy and record level privacy

for Omniglot dataset:

•   bash omniglot_DPAGRLR.sh
  

for mini_ImageNet dataset:

•   bash mini-imagenet_DPAGRLR.sh
  

for CIFAR-FS dataset:

•   bash cifar-fs_DPAGRLR.sh
  

4. Training non-private algorithm

for Omniglot dataset:

•   bash omniglot_MAML.sh
  

for mini_ImageNet dataset:

•   bash mini-imagenet_MAML.sh
  

for CIFAR-FS dataset:

•   bash cifar-fs_MAML.sh
  

These command will produce the test accuracy shown in Table 2 of the paper.

We are running the code on a server equipped with a 3.3 GHz Intel Core i9-9820X CPU, and a GeForce RTX 2080 Ti GPU. The running time of DPAGR

The running time of DPAGRLR will be a little lower than DPAGR.

Different GPU would lead to quite different running time.