---
title: "Federated Learning"
---

# Federated Learning

Privacy-preserving distributed machine learning directly in the database.

## Overview

HeliosDB Federated Learning enables:
- Distributed ML training across nodes without sharing raw data
- Privacy-preserving model aggregation
- Differential privacy guarantees
- Integration with database queries

## Quick Start

```sql
-- Create a federated learning job
CREATE FEDERATED LEARNING JOB fraud_detection
  MODEL TYPE 'logistic_regression'
  USING (SELECT features, label FROM transactions)
  WITH (
    rounds = 10,
    local_epochs = 5,
    privacy_budget = 1.0
  );

-- Start training
START FEDERATED JOB fraud_detection;

-- Check training status
SELECT * FROM helios_federated_jobs WHERE name = 'fraud_detection';
```

## Key Features

| Feature | Description |
|---------|-------------|
| **Privacy-Preserving** | Data never leaves local nodes |
| **Differential Privacy** | Configurable privacy guarantees |
| **Secure Aggregation** | Encrypted gradient aggregation |
| **Model Types** | Logistic regression, neural networks, XGBoost |
| **Auto-Scaling** | Automatic participant management |

## Documentation

| Document | Description |
|----------|-------------|

## Related

- **ML Integration**: `/docs/guides/user/ADVANCED_ML_INTEGRATION_GUIDE.md`
- **GPU Acceleration**: `/docs/guides/user/GPU_ACCELERATION_GUIDE.md`

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