Training AI Models While Preserving Data Privacy with Federated Learning

What is Federated Learning?

Federated Learning is a distributed learning technique where models are trained in each participant's local environment without centralizing data, sharing only model parameters.

Advantages of Federated Learning

How It Works

1. Global Model Distribution

The central server distributes the initial model to each participant.

2. Local Training

Each participant trains the model using their own data.

3. Parameter Aggregation

Trained model parameters are securely aggregated at the central server.

4. Model Update

The global model is updated with aggregated results and redistributed.

Application Areas

Conclusion

Federated learning is a technology that simultaneously achieves data utilization and privacy protection. Build secure collaborative AI with POLYGLOTSOFT's AI platform.