What is it?
Federated Learning (FL) is a machine learning technique that allows training an artificial intelligence model on a decentralized dataset while personal data remains on users' devices.
Simply put, instead of collecting all sensitive user data in one central cloud (as Google or Apple do), FL sends the learning model to the data rather than the other way around.
How does it work?
The FL process includes the following stages:
Model Distribution: The central model (e.g., neural network) is sent to the devices of many users (smartphones, computers).
Local Learning: Each device locally trains this model on its own private data. This data never leaves the device.
Aggregation of Updates: The device sends only updates (weights/parameters) of the model — not raw data — back to the central server (or blockchain).
Creating the Final Model: The central server aggregates (averages) all these local updates, creating a single, high-accuracy, and decentralized trained model.
How is FL related to Web3?
Blockchain and tokens are the perfect tools for organizing and incentivizing federated learning:
Incentives (Tokenomics): Users receive tokens as a reward for providing their computational power and private data for training the model. This creates a tokenized data economy.
Verification: Blockchain can be used for a transparent and immutable record of the training process and to verify that participants have honestly provided updates (preventing fraud).
Decentralization: FL, combined with decentralized computing (which we have already discussed), allows for the creation of AI models that are owned by no one and that no one can control or censor.
Federated Learning is key to creating a future where AI can evolve without sacrificing the fundamental right of users to data privacy.
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