Frameworks d'apprentissage fédéré
For un framework pour l'apprentissage fédéré, the strongest matches are federatedai/fate (FATE is a comprehensive, open-source federated learning platform that), adap/flower (Flower is a purpose-built federated learning framework that orchestrates) and project-monai/monai (MONAI is a PyTorch-based deep learning framework for healthcare). openmined/pysyft and tensorflow/federated round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Ces bibliothèques permettent l'entraînement collaboratif de modèles de machine learning tout en conservant les données brutes sensibles sur les appareils locaux.
Frameworks d'apprentissage fédéré
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- federatedai/fate
FederatedAI/FATE
6,048Voir sur GitHubFATE is an open-source federated learning platform that enables multiple organizations to collaboratively train machine learning models without exposing raw data to any party. It provides a complete framework for private data collaboration, allowing participants to jointly compute on sensitive information while maintaining data privacy and security guarantees through secure multi-party computation protocols. The platform distinguishes itself through its comprehensive infrastructure management capabilities, supporting automated deployment of multi-party clusters using Ansible-driven provisioni
FATE is a comprehensive, open-source federated learning platform that lets multiple parties collaboratively train models without sharing raw data, and its support for secure aggregation protocols and multi-party deployment aligns well with the requested features like secure aggregation, cross-silo setup, and simulation.
PythonSecure Aggregation ProtocolsFederated Learnings - adap/flower
adap/flower
6,971Voir sur GitHubFlower is a federated learning framework and distributed machine learning orchestrator designed to train models across decentralized devices. It functions as a privacy-preserving toolkit that enables model training and data analysis on local hardware, ensuring raw data remains on the device while contributing to a synchronized global model. The system employs an agnostic wrapper and integrator to connect diverse machine learning libraries, allowing different frameworks to operate within the same training loop. It uses a remote procedure call orchestrator to manage the exchange of model weight
Flower is a purpose-built federated learning framework that orchestrates decentralized training across devices and silos, supports TensorFlow and PyTorch, includes privacy-preserving techniques like secure aggregation and differential privacy, and provides simulation and benchmarking tools — exactly meeting this search's requirements.
PythonFederated OrchestrationPrivacy-Preserving Model TrainingAI Client-Server Communication Protocols - project-monai/monai
Project-MONAI/MONAI
7,869Voir sur GitHubMONAI is a PyTorch-based deep learning framework and library specifically designed for healthcare imaging. It provides a suite of domain-specific neural network architectures, specialized loss functions, and preprocessing pipelines tailored for analyzing multi-dimensional medical data. The project distinguishes itself through a decentralized federated learning system that allows models to learn from datasets across multiple institutions without exchanging raw patient images. It also features AI-assisted medical image annotation tools and a standardized model bundling system to ensure consiste
MONAI is a PyTorch-based deep learning framework for healthcare imaging that includes a decentralized federated learning system for training across institutions without sharing raw data — it fits the federated learning category but is specialized for medical imaging and does not explicitly cover TensorFlow support or differential privacy.
PythonFederated Learnings - openmined/pysyft
OpenMined/PySyft
9,907Voir sur GitHubPySyft is a privacy-preserving machine learning framework and remote computation engine. It functions as a decentralized data analysis orchestrator that allows for the execution of data science workflows on remote servers without requiring the transfer of raw private data from the host device. The platform provides a secure collaboration environment where data owners manage permissions and authorize specific collaborators to run computations. It differentiates its workflow by utilizing mock data for local development and validation before submitting final analysis jobs to private remote serve
PySyft is a privacy-preserving machine learning framework that supports federated learning and remote computation on decentralized data without sharing raw data, fitting the core need, and it integrates with PyTorch for model training, though TensorFlow support and specific features like simulation/benchmarking are not clearly indicated.
PythonPrivacy-Preserving Machine LearningRemote Data AnalysisData and Resource Permissions - tensorflow/federated
tensorflow/federated
2,441Voir sur GitHubAn open-source framework for machine learning and other computations on decentralized data.
TensorFlow Federated is a dedicated framework for federated learning that lets you train models on decentralized data without sharing raw data, with strong TensorFlow integration and support for secure aggregation, differential privacy, cross-device/silo, and simulation, though its primary focus is TensorFlow rather than PyTorch.
PythonFederated LearningMachine Learning FrameworksPerception and Machine Learning - shaoxiongji/federated-learning
shaoxiongji/federated-learning
1,517Voir sur GitHubThis project is a research-oriented platform designed for simulating decentralized machine learning environments. It provides a framework for training models across multiple client nodes while keeping raw data localized, enabling the evaluation of model convergence and performance under various distributed network conditions. The system utilizes a parameter-server architecture to coordinate training, where a central coordinator manages the global model state and aggregates weight updates from distributed participants. By decoupling the training orchestration logic from the underlying neural n
This is a PyTorch-specific implementation of federated learning that lets you train models on decentralized data without sharing raw data, so it fits the category directly, though it lacks broader framework support (e.g., TensorFlow) and advanced features like secure aggregation or differential privacy.
PythonFederated Learning FrameworksFederated LearningParameter Servers - secretflow/secretflow
secretflow/secretflow
2,629Voir sur GitHubSecretFlow is a privacy computing framework and platform designed for secure multi-party computation, federated learning, and privacy-preserving data analysis across independent nodes. It provides a management system to coordinate secure workloads and cryptographic tasks across a distributed cluster. The project enables joint data analysis and machine learning on partitioned datasets using cryptographic protocols. It allows for the training of models and the execution of analytical queries across multiple parties without exposing raw source information to any single participant. The framewor
SecretFlow is a privacy computing framework that includes federated learning, secure multi-party computation, and differential privacy, enabling training on decentralized data without sharing raw data; it does not explicitly mention integration with TensorFlow or PyTorch, but it squarely belongs to the federated learning framework category.
PythonMulti-Party Computation FrameworksDistributed Model DevelopmentDistributed Privacy Queries
