easyFL enables researchers and practitioners to experiment with Federated Learning (FL) algorithms. It provides a user-friendly framework for conducting various experiments and evaluating different approaches. The library offers modules for building and running FL tasks, handling data partitioning, and evaluating results.
Primary technology: Python, PyTorch; Approach: provides a flexible and modular structure for FL experimentation.
easyFL offers a straightforward API for defining and running FL experiments. It includes a customizable simulator to model real-world system heterogeneities (e.g., client unavailability, slow responses) which is crucial for realistic evaluation. A comprehensive benchmark gallery supports diverse data types and scenarios, enabling thorough testing and comparison of algorithms. The design emphasizes modularity and extensibility, supporting custom algorithms and evaluation metrics.
- Task Definition: Simplifies the creation of federated learning tasks with flexible data partitioning and model configurations.
- Simulator: Includes a customizable simulator to model system heterogeneity, reflecting real-world challenges in distributed environments.
- Benchmarks: Offers a rich collection of benchmarks covering various datasets and scenarios (classification, detection, segmentation, etc.).
- Evaluation: Facilitates comprehensive evaluation with built-in tools for analyzing training results and visualizing performance metrics.
- Extensibility: Designed to be extensible, allowing users to easily integrate custom algorithms, data preprocessing steps, and evaluation metrics.
- User-Friendly API: Features a Pythonic and easy-to-use API, making it accessible to researchers with varying levels of experience.
- Flexible Scenario Configuration: Supports various FL scenarios, including horizontal and vertical partitioning, and various distributed settings.
easyFL is actively maintained, with recent commits indicating ongoing development and feature additions. The documentation is still developing. A benchmarking gallery and tutorial are available. Community engagement is primarily through a WeChat group. The project has a good number of stars and forks which suggests user interest.
easyFL benefits researchers, students, and practitioners seeking a simple yet powerful tool for exploring Federated Learning. It is ideal for evaluating new FL algorithms, studying the impact of system heterogeneity, and benchmarking performance on a variety of datasets. It offers a streamlined approach compared to manually setting up FL experiments.