Federated Learning with Model Distillation on the vca cluster

Usage

Run on local machine

1.) In exec.sh define paths

RESULTS_PATH="results/"
DATA_PATH="/path/to/where/you/store/your/datasets"
CHECKPOINT_PATH="checkpoints/"

2.) and set the hyperparameters

hyperparameters="[{...}]"

3.) Run via

bash exec.sh

Run on vca cluster

1.) In your home directory on the server, create a folder base_images/, containing the file pytorch15.def:

<<<<<<<<<<<< pytorch15.def >>>>>>>>>>>
Bootstrap: docker
From: pytorch/pytorch:1.6.0-cuda10.1-cudnn7-runtime

%post
export "PATH=/opt/conda/bin:$PATH"

conda install matplotlib
conda install numpy
conda install scipy
conda install tqdm
<<<<<<<<<<<< pytorch15.def >>>>>>>>>>>

2.) Run

singularity build --force --fakeroot pytorch15.sif pytorch15.def

3.) Create a folder in_ram_data/ where you save your data sets (CIFAR, MNIST, ..)

4.) Change email address:

#SBATCH --mail-user=your.mail@hhi.fraunhofer.de

5.) Run via

  sbatch exec.sh

6.) You can check if everything is working

  watch tail -n 100 out/<SLURM_JOB_ID>.out 

Hyperparameters

Task

• "dataset" : Choose from ["mnist", "cifar10"]
• "distill_dataset" : Choose from ["stl10"],
• "net" : Choose from ["mobilenetv2", "lenet_mnist", "lenet_cifar", "vgg11", "vgg11s"]

Federated Learning Environment

• "n_clients" : Number of Clients
• "classes_per_client" : Number of different Classes every Client holds in it's local data, 0 returns an iid split
• "participation_rate" : Fraction of Clients which participate in every Communication Round
• "batch_size" : Batch-size used by the Clients
• "balancedness" : Default 1.0, if <1.0 data will be more concentrated on some clients
• "communication_rounds" : Total number of communication rounds
• "local_epochs" : Local training epochs at every client
• "distill_epochs" : Number of epochs used for distillation
• "n_distill" : Size of the distilation dataset
• "distill_mode" : The distillation mode, chosse from ("regular", "pate", "pate_up", ..)
• "aggregation_mode" : Choose from "FA" (Federated Averaging), "FD" (Federated Distillation), "FAD" (FA + FD)
• "pretrained" : Load a pretrained model from the /checkpoints directory according to the distillation data that is used, e.g. {"stl10" : "simclr_net_bn_stl10_80epochs.pth"}

Logging

• "log_frequency" : Number of communication rounds after which results are logged and saved to disk
• "log_path" : e.g. "results/experiment1/"

Run multiple experiments by listing different configurations, e.g.

`"n_clients" : [10, 20, 50]`.

Logging and Visualizing

In federated_learning.py, calling

xp.log(dict)

will save experiment results under given keys. Every experiment produces a summary which is stored in the directory specified in "log_path". You can import all experiments stored in a certain directory via

import experiment_manager as xpm
list_of_experiments = xpm.get_list_of_experiments(path)

list_of_experiments contains Experiment objects with the hyperparameters and the results

xp.hyperparameters
xp.results

of the respective experiments.