Myth: Federated learning requires significant data sharing b

Myth: Federated learning requires significant data sharing between participants, compromising individual data confidentiality.
Reality: Federated learning is designed to minimize data sharing. It does so by only sharing model updates, not data itself, allowing for global knowledge sharing while keeping individual participant data locally stored.

In federated learning, clients train models locally on their data and then send updated model weights to a central server. The server aggregates these updates and computes a new global model. This process enables collective learning without compromising individual data.

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