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On Privacy and Personalization in Cross-Silo Federated Learning

This repo hosts the code for the paper "On Privacy and Personalization in Cross-Silo Federated Learning". The implemention is written in Python with JAX and Haiku. A short version of this work appears at TPDP 2022.

[PDF][arXiv]

Directory structure

  • main.py: the main driver script
  • utils.py: helper functions for reading datasets, data loading, logging, etc.
  • jax_utils.py: helper functions for JAX (loss, prediction, structs, etc)
  • model_utils.py: model definitions in JAX/Haiku
  • dp_accounting.py helper functions for privacy accounting
  • data_utils/: scripts for preprocessing datasets (mainly rotated + masked MNIST and ADNI)
  • trainers/: implementations of the benchmark algorithms
  • runners/: scripts for starting an experiment
  • data/: directory of datasets

Dependencies

Python 3.9 with dependencies in requirements.txt (an older version of Python would probably work too).

Running an experiment

The general template commands for running an experiment are:

  • Without DP
bash runners/<dataset>/run_<algorithm>.sh --repeat 5 [other flags]
  • With DP
bash runners/<dataset>/dp/run_<algorithm>.sh --repeat 5 -eps <epsilon value> [other flags]

Below are examples commands for running experiments with some hyperparameters. See main.py for the full list of hyperparameters.

Example: single run

For example, to reproduce the results on School of Fig. 3(c) at $\varepsilon = 6$, run

bash runners/school/dp/run_fedavg.sh -r 5 -t 200 -eps 6 -lr 0.01  # gives ~ 0.02564
bash runners/school/dp/run_local.sh -r 5 -t 200 -eps 6 -lr 0.01   # gives ~ 0.02628
bash runners/school/dp/run_mrmtl.sh -r 5 -t 200 -eps 6 --lambda 1 -lr 0.01  # gives ~ 0.02394

Example: hyperparameter sweep

For convenience, hyperparameter grid search is implemented for --lambda / --lambdas (personalization hyperparam for MR-MTL, Ditto, etc.) and --learning_rate / --lrs (client learning rates).

For example, we can sweep the client LRs and the lambdas of MR-MTL for a range of epsilons via

for eps in 0.1 0.3 0.5 0.7 1; do
  bash runners/vehicle/dp/run_mrmtl.sh \
    -r 5 --lrs "0.001 0.003 0.01" --lambdas "0.1 0.3 1 3" \
    -t 400 --quiet -eps $eps \
    -o logs/vehicle/mrmtl_eps$eps
done

The results will then be stored in logs/vehicle/mrmtl_eps<eps>.

Dataset access

See appendix of the paper for full details on dataset access.

  • School, Vehicle, and GLEAM datasets can be downloaded here. A copy of the School dataset is included for convenience.
  • Rotated/Patched MNIST can be downloaded/preprocessed automatically from the data_utils/rotated_mnist.py script.
  • ADNI dataset requires approval; see appendix for more details.
  • Put datasets in the following structure: data/<dataset_name>/<content>; e.g. data/vehicle/vehicle.mat.

Mean Estimation and Privacy Cost of Hyperparameter Tuning

To generate Fig. 7 of the paper, run

python3 plot_hparam_cost.py -eps 0.25 -tau 0.03 -o lowhet  # Low heterogeneity
python3 plot_hparam_cost.py -eps 0.25 -tau 0.08 -o midhet  # Moderate heterogeneity
python3 plot_hparam_cost.py -eps 0.25 -tau 0.2 -o highhet  # High heterogeneity

Citation

Please consider citing our work if you find this repo useful:

@article{liu2022privacy,
  title={On privacy and personalization in cross-silo federated learning},
  author={Liu, Ken and Hu, Shengyuan and Wu, Steven Z and Smith, Virginia},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={5925--5940},
  year={2022}
}