FederatedAI/FATE
FATE
FATE 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 provisioning and cloud-native technologies like containers and Kubernetes. FATE includes a DAG-based pipeline scheduler for orchestrating federated tasks, an Eggroll distributed compute engine for distributed data processing, and a federated model serving proxy for routing inference requests with privacy-preserving transformations. The system implements intersection-safe aggregation protocols and a party-role-based topology that assigns participants specific roles (guest, host, arbiter) to define data access and computation permissions.
Beyond core training and serving, FATE offers capabilities for deploying standalone instances for local development, running on ARM architecture, and managing federated infrastructure through release artifacts and Docker containers. The platform also provides visualization tools for exploring model behavior and performance.
Features
- Privacy-Preserving Model Training - Trains machine learning models collaboratively across multiple parties without exposing raw data.
- Federated Learning Frameworks - An open-source platform for collaborative machine learning across multiple parties without exposing raw data.
- Federated Infrastructure Managers - A tool for deploying, configuring, and managing federated learning clusters using automation and cloud-native technologies.
- Federated Orchestration - Orchestrates multi-party secure task execution for collaborative machine learning workflows.
- Secure Aggregation Protocols - Combines encrypted model updates using secure aggregation so no single party can see another's raw data.
- Federated Learning Frameworks - A framework that enables multiple organizations to jointly train machine learning models while keeping data private.
- Federated Learnings - Trains machine learning models collaboratively across multiple parties without sharing raw data.
- Collaborative Training - Training machine learning models collaboratively across multiple organizations without exposing raw data to any party.
- Distributed Computing Engines - Runs distributed data processing and computation across parties using a custom cluster manager.
- Privacy-Preserving Compute Engines - An engine that orchestrates secure multi-party computation tasks for privacy-preserving data analysis.
- DAG-Based Orchestration - Executes federated tasks as a directed acyclic graph of stages for parallel and sequential multi-party computation.
- Federated Infrastructure Operators - Operates the platform using cloud-native technologies like containers and orchestration systems.
- Federated Cluster Deployers - Setting up a distributed cluster of multiple parties using automation tools for collaborative model training.
- Federated Cluster Provisioners - Automates deployment of multi-party clusters using Ansible playbooks for federated learning infrastructure.
- Model Serving - Deploys trained models into production for high-performance inference across participating parties.
- Federated Model Serving Proxies - Routes inference requests to the correct party's model endpoint with privacy-preserving transformations.
- Federated Role Topologies - Assigns each participant a role (guest, host, arbiter) defining data access and computation permissions.
- Secure Data Collaboration - Enabling multiple parties to jointly compute on sensitive data while maintaining data privacy and security guarantees.
- Model Visualization - Explores and understands model behavior and performance using visualization tools.
- Deployment Automation - Automates configuration and deployment operations for a collaborative machine learning platform.
- Docker Container Deployments - Runs the platform inside a container for isolated and reproducible environments.
- Module-Based Registries - Loads algorithm modules from a registry so pipelines can be composed without code changes.
- Federated Learning - Industrial-grade framework for federated learning.
- Privacy and Safety - Industrial-grade framework for federated learning.
