ProFed 0.7.6

A benchmark for proximity-based non-IID Federated Learning

ProFed: A Benchmark for Proximity-based Federated Learning

🔗 ProFed: A Benchmark for Proximity‑based Non‑IID Federated Learning

ProFed is a framework for evaluating federated learning (FL) systems under realistic, geographically clustered, non‑IID data scenarios. It simulates clients grouped into regions such that data is IID within regions but non‑IID across regions.

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🚀 Features

• Built‑in datasets: Support for MNIST, FashionMNIST, CIFAR‑10, CIFAR‑100 and UTKFace via PyTorch/TorchVision.
• Flexible partitioning: Implements Dirichlet-based splits, hard label skews, and can model arbitrary proximity-driven distribution skews.
• Customizable proximity modeling: Define how many geographic clusters (regions) to simulate and control skew intensity (e.g., Dirichlet α).

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🔧 Getting Started

Prerequisites

• Python ≥ 3.12

Installation

ProFed is publicly released on PyPi, to install ProFed on your machine:

pip install ProFed

API Explanation

1. Downloading and importing the dataset

train_data, test_data = download_dataset('EMNIST')

2. Splitting into train & validation sets

train_data, validation_data = split_train_validation(train_data, 0.8)

3. Partitioning into geographic “regions” (i.i.d. internally)

environment = partition_to_subregions(
    train_data,
    validation_data,
    dataset_name = 'EMNIST',
    partitioning_method = 'Hard',
    number_of_regions = 5,
    seed = 42,
)

• method: partition strategy ('Hard', 'Dirichlet', or 'IID')

• number_of_regions: how many simulated geographic clusters

• dirichlet_alpha: optional, controls how concentrated the Dirichlet splits are

• min_region_size: optional, retries Dirichlet sampling until each region has at least this many samples

• Returns an Environment object. Each region within it contains IID data internally, but non-IID across regions.

Example for Dirichlet:

environment = partition_to_subregions(
    train_data,
    validation_data,
    dataset_name = 'CIFAR100',
    partitioning_method = 'Dirichlet',
    number_of_regions = 8,
    seed = 42,
    dirichlet_alpha = 0.3,
    min_region_size = 20,
)

1. Distributing region data across devices

mapping = {}
for region_id, devices in mapping_devices_area.items():
    mapping_devices_data = environment.from_subregion_to_devices(
        region_id,
        len(devices)
    )
    for device_index, data in mapping_devices_data.items():
        device_id = devices[device_index]
        mapping[device_id] = data

Splits the region’s IID data equally among its devices, assigning each a local subset.

The result is a mapping:

device_id → local_dataset

✍🏻 Examples of usage

1. Proximity-based Self-Federated Learning
2. SParSeFuL
3. Baselines implementation
4. ProFed Startup Experiemnts

📄 License

MIT License — feel free to freely use, modify, and distribute.

📬 Contact

For questions, issues, or contributions, feel free to reach out:

• Author: Davide Domini
• Email: [e-mail]
• GitHub: domm99

You can also open an issue or submit a pull request on GitHub!