FastLloyd

FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy FastLloyd is an approach to privacy-preserving k-means clustering in horizontally federated settings. It offersstate-of-the-art utility while providing formal privacy guarantees through differential privacy, and achieves orders ofmagnitude faster performance compared to previous privacy-preserving clustering methods. Overview This repository implements the FastLloyd protocol described in the paper "FastLloyd: Federated, Accurate, Secure, andTunable k-Means Clustering with Differential Privacy". FastLloyd addresses the challenging problem of collaborativeclustering across multiple data owners without compromising privacy, through: 1. A novel differentially private k-means algorithm with radius constraints2. A lightweight secure aggregation protocol for federated settings Installation Requirements - Python 3.8 or higher- Open MPI (for multiparty communication)- Required Python packages listed in `env.yml` Setup 1. Download the repoistory 2. Extract the data and navigate to code directory: mkdir -p datafor f in *.tar.xz; do tar --extract --xz --file="$f" --directory=datadonemv data D-Diaa-FastLloyd-879df3a/cd D-Diaa-FastLloyd-879df3a/ 3. Create and activate the conda environment: conda env create -f env.ymlconda activate fastlloyd Usage Running Experiments FastLloyd supports multiple experiment types: 1. **Accuracy**: Evaluate clustering quality across different privacy settings python experiments.py --exp_type "accuracy" 2. **Scale**: Analyze scalability with dataset size, dimensions, and number of clusters python experiments.py --exp_type "scale" 3. **Timing**: Measure communication and computation time mpirun -np 3 python experiments.py --exp_type "timing" You can also use the provided scripts to run multiple experiment types: bash scripts/experiment_runner.sh # For accuracy and scale experimentsbash scripts/timing_runner.sh # For timing experiments with varying numbers of clients Visualization The repository includes several visualization tools in the `plots` directory: - `per_dataset.py`: Creates performance visualizations for individual datasets- `scale_heatmap.py`: Generates heatmaps to analyze scalability- `synthetic_bar.py`: Creates bar plots comparing performance on synthetic datasets- `ablation_plots.py`: Creates plots for ablation studies- `timing_analysis.py`: Analyzes and reports execution timing data Customization You can customize various aspects of the experiments through the argument parser in `experiments.py`: python experiments.py --exp_type "test" --datasets "mnist" "adult" --method "diagonal_then_frac" --alpha 0.8 --post "fold" --results_folder "my_results" Key parameters include: - `--exp_type`: Type of experiment to run (accuracy, scale, timing, test)- `--datasets`: Datasets to use for the experiment- `--method`: Maximum distance method to use- `--alpha`: Maximum distance parameter- `--post`: Post-processing method for centroids- `--results_folder`: Folder to store results Citation If you use FastLloyd in your research, please cite the paper: @inproceedings{diaa2025fastlloyd, title={FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy}, author={A. Diaa and T. Humphries and F. Kerschbaum}, eventtitle = {The 34th {USENIX} Security Symposium}, year={2025},}

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