Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off (ICML 2025)

📣 01/05/25: This paper has been accepted to ICML 2025!

The implementation of our paper:

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off (FedCEO)

Dependence

To install the dependencies: pip install -r requirements.txt.

Data

The EMNIST and CIFAR10 datasets are downloaded automatically by the torchvision package.

Usage

We provide scripts that have been tested to produce the results stated in our paper (utility experiments and privacy experiments). Please find them in the file: train.sh.

Flags

• FL related

  • args.epochs: The number of communication rounds.
  • args.num_users: The number of total clients, denoted by $N$.
  • args.frac: The sampling rate of clients, denoted by $p$.
  • args.lr: The learning rate of local round on the clients, denoted by $\eta$.
  • args.privacy: Adopt the DP Gaussian mechanism or not.
  • args.noise_multiplier: The ratio of the standard deviation of the Gaussian noise to the L2-sensitivity of the function to which the noise is added.
  • args.flag: Using our low-rank processing or not.

• FedCEO related

  • args.lamb: The weight of regularization term, denoted by $\lambda$.
  • args.interval: The smoothing interval to adopt, denoted by $I$.
  • args.flag: The common ratio of the geometric series, denoted by $\vartheta$.

• Model related

  • args.model: MLP or LeNet.

• Experiment setting related

  • args.dataset: cifar10 or emnist.
  • args.index: The index for leaking images on Dataset.

Citation

@article{li2024clients,
  title={Clients collaborate: Flexible differentially private federated learning with guaranteed improvement of utility-privacy trade-off},
  author={Li, Yuecheng and Wang, Tong and Chen, Chuan and Lou, Jian and Chen, Bin and Yang, Lei and Zheng, Zibin},
  journal={arXiv preprint arXiv:2402.07002},
  year={2024}
}