Question

In a Federated Learning environment, what specific object is sent from the edge node to the central server during the model update process instead of raw user data?

Accepted Answer

In a Federated Learning environment, the specific object sent from the edge node to the central server is the model update, which consists of calculated gradients or updated model weights. To understand this, consider that an edge node, such as a smartphone or IoT device, holds local raw data that must remain private. Instead of sending this data, the device performs a local training round on its own data to improve the global model. During this training, the machine learning algorithm calculates gradients, which are mathematical values representing the direction and magnitude by which the model parameters must change to reduce prediction errors. The edge node transmits these resulting numerical updates or weight adjustments to the central server. The server then aggregates these updates from many devices using an algorithm like Federated Averaging to create a new, improved global model, which is then sent back to the edge nodes. By sending only these mathematical updates instead of the raw data, the system ensures that sensitive user information never leaves the local device, preserving data privacy while still allowing the model to learn from decentralized sources.

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In a Federated Learning environment, what specific object is sent from the edge node to the central server during the model update process instead of raw user data?