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  • 1
    Awesome-FL

    Awesome-FL

    Comprehensive and timely academic information on federated learning

    A “awesome” curated list of federated learning (FL) academic resources: research papers, tools, frameworks, datasets, tutorials, and workshops. A hub for FL knowledge maintained by the academic community.
    Downloads: 0 This Week
    Last Update:
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  • 2
    FL4Health

    FL4Health

    Library to facilitate federated learning research

    FL4Health is a Vector Institute toolkit for building modular, clinically-focused FL pipelines. Tailored for healthcare, it supports privacy-preserving FL, heterogeneous data settings, integrated reporting, and clear API design.
    Downloads: 2 This Week
    Last Update:
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  • 3
    FLEXible

    FLEXible

    Federated Learning (FL) experiment simulation in Python

    FLEXible (Federated Learning Experiments) is a Python framework offering tools to simulate FL with deep learning. It includes built-in datasets (MNIST, CIFAR10, Shakespeare), supports TensorFlow/PyTorch, and has extensions for adversarial attacks, anomaly detection, and decision trees.
    Downloads: 1 This Week
    Last Update:
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  • 4
    Appfl

    Appfl

    Advanced Privacy-Preserving Federated Learning framework

    APPFL (Advanced Privacy-Preserving Federated Learning) is a Python framework enabling researchers to easily build and benchmark privacy-aware federated learning solutions. It supports flexible algorithm development, differential privacy, secure communications, and runs efficiently on HPC and multi-GPU setups.
    Downloads: 4 This Week
    Last Update:
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  • 5
    Substra

    Substra

    Low-level Python library used to interact with a Substra network

    An open-source framework supporting privacy-preserving, traceable federated learning and machine learning orchestration. Offers a Python SDK, high-level FL library (SubstraFL), and web UI to define datasets, models, tasks, and orchestrate secure, auditable collaborations.
    Downloads: 0 This Week
    Last Update:
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  • 6
    NVIDIA FLARE

    NVIDIA FLARE

    NVIDIA Federated Learning Application Runtime Environment

    NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. NVIDIA FLARE is built on a componentized architecture that allows you to take federated...
    Downloads: 1 This Week
    Last Update:
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  • 7
    Xfl

    Xfl

    An Efficient and Easy-to-use Federated Learning Framework

    XFL is a lightweight, high-performance federated learning framework supporting both horizontal and vertical FL. It integrates homomorphic encryption, DP, secure MPC, and optimizes network resilience. Compatible with major ML libraries and deployable via Docker or Conda.
    Downloads: 0 This Week
    Last Update:
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  • 8
    FedLab

    FedLab

    A flexible Federated Learning Framework based on PyTorch

    A Python-based framework for federated learning simulation, emphasizing modularity, communication efficiency, and algorithmic flexibility. Supports both server- and client-side customization for research and development purposes.
    Downloads: 3 This Week
    Last Update:
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  • 9
    Fedhf

    Fedhf

    A Flexible Federated Learning Simulator

    FedHF is a Python-based simulator for flexible, heterogeneous, and asynchronous federated learning research. It provides configurable resource models, supports asynchronous protocols, and accelerates experimentation.
    Downloads: 1 This Week
    Last Update:
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