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Federated Learning on GLIN

Train machine learning models across distributed data sources without sharing raw data.

What is Federated Learning?​

Federated Learning is a machine learning technique that trains models across decentralized devices or servers holding local data samples, without exchanging the raw data. GLIN Network provides the infrastructure to:

  • 🤖 Create Training Tasks - Define models and requirements
  • 💻 Distributed Training - Train across global providers
  • 🔐 Privacy-Preserving - Data never leaves local devices
  • 💰 Incentivized - Reward providers with GLIN tokens
  • 🎯 Decentralized - No central authority required

How It Works​

1. Task Creator → Post training task on-chain
2. Providers → Download model, train on local data
3. Submit → Upload encrypted gradients to network
4. Aggregator → Combine gradients into global model
5. Rewards → Distribute GLIN tokens to contributors

Use Cases​

Healthcare​

Train models on medical data across hospitals without sharing patient records.

Finance​

Build fraud detection models using data from multiple banks while maintaining confidentiality.

IoT & Edge Computing​

Train models on device data (smartphones, sensors) without uploading raw data to cloud.

Collaborative Research​

Enable researchers to collaborate on ML models without sharing proprietary datasets.

Quick Start​

Install the Client​

npm install @glin-ai/federated

Create Your First Task​

import { FederatedClient } from '@glin-ai/federated';

const client = await FederatedClient.connect();

const task = await client.createTask({
  name: 'Image Classifier',
  model: 'resnet50',
  rounds: 10,
  minProviders: 5,
  rewardPerRound: '100 GLIN'
});

console.log('Task created:', task.id);

Architecture​

  • Task Registry - On-chain task definitions and status
  • Gradient Storage - Encrypted gradient uploads
  • Aggregation - Secure multi-party computation
  • Rewards - Automatic token distribution
  • Verification - Quality checks and fraud prevention

Key Concepts​

Getting Started​

  1. Create a Task
  2. Train a Model
  3. Deploy Model

Examples​

Ready to build? Start with your first task →