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Federated Learning, One way to solve for AI having difficulty making

Curiosity: How can we train ML models on private data without centralizing it?

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By Jang Young Jeong 3 min read

Federated Learning: Training on Private Data Without Centralization

Curiosity: How can we train ML models on private data without centralizing it? What happens when we bring the model to the data instead of bringing data to the model?

One of the main factors preventing AI adoption in healthcare and banking is data privacy requirements. Hospitals and banks are reluctant to share sensitive data. Federated Learning solves this by training models on distributed data without centralization.

How to train on private data

The Challenge

Retrieve: Why data privacy blocks AI adoption.

Problem:

  • Healthcare and banking require strict data privacy
  • Regulations make data sharing risky
  • Benefits don’t outweigh risks
  • Models can’t access training data

Impact: AI struggles to enter regulated industries.

Federated Learning Solution

Innovate: Bringing models to data instead of data to models.

Key Concept: Instead of bringing data to the model, bring the model to the data.

Real-World Examples:

  • Google’s query suggestions on Android (Gboard)
  • Self-driving car continuous training
  • Healthcare model training
  • Banking fraud detection

Federated Learning Process

Retrieve: How federated learning works.

graph TB
    A[Central Server] --> B[Pre-trained Model]
    B --> C[Device 1]
    B --> D[Device 2]
    B --> E[Device N]
    
    C --> F[Local Training]
    D --> G[Local Training]
    E --> H[Local Training]
    
    F --> I[Gradients/Model]
    G --> I
    H --> I
    
    I --> J[Aggregation]
    J --> K[Updated Model]
    K --> A
    
    style A fill:#e1f5ff
    style F fill:#fff3cd
    style K fill:#d4edda

Step-by-Step Process

Retrieve: Detailed federated learning workflow.

StepProcessPurpose
1. Pre-trainingCentral server pre-trains model⬆️ Initial model
2. DistributionSend model to user devices⬆️ Local deployment
3. Local TrainingUsers interact, model fine-tunes locally⬆️ Privacy-preserving
4. AggregationSend gradients/models back to server⬆️ Centralized learning
5. AveragingAverage updates into one model⬆️ Global improvement
6. SynchronizationSync updated model to devices⬆️ Continuous learning

Key Benefit: Train on data from millions of users without ever storing the data.

Advantages

Innovate: Why federated learning matters.

AdvantageDescriptionImpact
PrivacyData never leaves devices⬆️ Compliance
RegulationMeets privacy requirements⬆️ Adoption
ScaleMillions of users⬆️ Training data
EfficiencyDistributed training⬆️ Resource use

Industry Adoption

Retrieve: Growing trend in federated learning.

Trend: With increasing data regulations, federated learning usage is growing.

Major Investors:

  • Google: Significant investment in federated learning
  • Meta: Developing federated learning technology

Applications:

  • Healthcare (patient data privacy)
  • Banking (financial data protection)
  • Mobile devices (on-device learning)
  • IoT (edge device training)

Resources

Retrieve: Tools and frameworks for federated learning.

Resources:

Key Takeaways

Retrieve: Federated learning enables training on private data by bringing models to data sources instead of centralizing data, solving privacy challenges in regulated industries.

Innovate: By implementing federated learning, you can train models on data from millions of users without storing or centralizing sensitive data, enabling AI adoption in healthcare, banking, and other privacy-sensitive domains.

Curiosity → Retrieve → Innovation: Start with curiosity about privacy-preserving ML, retrieve insights from federated learning approaches, and innovate by implementing distributed training that respects data privacy regulations.

Next Steps:

  • Explore TensorFlow Federated
  • Try OpenFL
  • Design federated architecture
  • Deploy federated systems
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