FederatedAI/FATE
An Industrial Grade Federated Learning Framework
Implements secure multi-party computation using homomorphic encryption and MPC protocols to enable collaborative model training across distributed parties without exposing raw data. Provides standardized algorithm components (logistic regression, tree-based models, deep learning, transfer learning) with pluggable scheduling engines through FATE-Flow, and integrates with Kubernetes via KubeFATE for production deployment at enterprise scale.
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Language
Python
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Apache-2.0
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Last pushed
Nov 19, 2024
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