Federated Learning: Decentralized AI Training
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AI Development ServicesWhat is Federated Learning?
Federated learning is a decentralized approach to training machine learning models. Instead of collecting all data in a central repository, the model is sent to where the data lives—on user devices or local servers. Each device trains a local version of the model using its own data. Only the learned insights (model updates or gradients), not the raw data itself, are sent back to a central server to improve the global model.
How does Federated Learning Work?
1. **Global Model Initialization**: A central server creates an initial, baseline model. 2. **Distribution**: The global model is sent to participating devices (nodes). 3. **Local Training**: Each device trains the model locally using its own stored data. 4. **Update Aggregation**: Devices send their model updates (not data) back to the central server. 5. **Global Model Update**: The server aggregates these updates (e.g., by averaging them) to create an improved global model, and the cycle repeats.
Key Use Cases for Federated Learning
Hospitals can collaborate to train predictive models for patient outcomes or disease detection without sharing sensitive patient health records, complying with HIPAA and other privacy regulations.
Smartphones use federated learning to improve predictive text, voice recognition, and personalized recommendations locally, ensuring user data like text messages and voice recordings never leave the device.
/ FAQ
Traditional ML requires all training data to be gathered in one central location. Federated learning keeps data decentralized and only centralizes the model updates.
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