Overview

Why Choose BlazeFL?

• 🚀 High Performance: Optimized for single-node simulations, BlazeFL allows you to adjust the degree of parallelism. For example, if you want to simulate 100 clients on a single node but lack the resources to run them all concurrently, you can configure 10 parallel processes to manage the simulation efficiently.

• 🔧 Extensibility: BlazeFL provides interfaces solely for communication and parallelization, avoiding excessive abstraction. This design ensures that the framework remains flexible and adaptable to various use cases.

• 📦 Minimal Dependencies: The core components of BlazeFL rely only on PyTorch, ensuring a lightweight and straightforward setup.

• 🔄 Robust Reproducibility: Even in multi-process environments, BlazeFL offers utilities to save and restore seed states, ensuring consistent and reproducible results across simulations.

• 🏷️ Type Hint Support: The framework fully supports type hints, enhancing code readability and maintainability.

• 🔗 Loose Compatibility with FedLab: Inspired by FedLab, BlazeFL maintains a degree of compatibility, facilitating an easy transition to production-level implementations when necessary.

How BlazeFL Works

BlazeFL enhances performance by storing shared parameters on disk instead of shared memory, enabling efficient parameter sharing across processes, simplifying memory management, and reducing overhead.