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A curated list of references for MLOps, the practice of managing and automating machine learning workflows.
A Python library for secure and private machine learning using federated learning and differential privacy
Flower is a federated learning framework that enables machine learning on decentralized data.
A unified and scalable ML library for large-scale distributed training, model serving, and federated learning.
An integrated federated learning library for research and production use cases.
Comprehensive collection of federated learning resources (papers, frameworks, datasets, tutorials, etc.)
An easy-to-use federated learning platform for developers building AI/ML applications.
A PyTorch implementation of Federated Learning, a distributed machine learning technique.
An implementation of federated learning, a distributed machine learning technique, using PyTorch.
An open-source privacy-preserving computing platform for secure multi-party computation and federated learning.
Standardized datasets for 2D and 3D biomedical image classification
Federated learning library for distributed AI model training across multiple devices or servers.
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