Federated Learning

Federated Learning is a technique for training machine learning models in which the model is trained in a distributed manner across multiple devices or servers containing local data, without the need to centralize this data. This technique is particularly useful for preserving data privacy and security, as sensitive data does not move from its original locations.

These are the main steps in the federated learning process:

  1. Model Initialization: A machine learning model is initialized on a central server.
  2. Model Distribution: This initial model is distributed to several participating devices or servers (clients).
  3. Local Training: Each client trains a copy of the model with its local data. This step is performed independently on each client.
  4. Model Update: The parameters of the locally trained model are sent back to the central server, instead of sending the data.
  5. Aggregation: The central server aggregates (combines) the updates from the models received from all clients to improve the central model.
  6. Iteration: This process is repeated over several rounds, with the centralized model being redistributed to the clients, trained locally, and the updates aggregated.
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