How can model aggregation be achieved in Federated Learning?

Instruction: Describe the process of model aggregation in Federated Learning and its significance.

Context: Aimed at assessing the candidate's understanding of the central server's role in aggregating locally trained models to form a global model, highlighting the importance of this step in achieving a comprehensive learning outcome.

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The way I'd approach it in an interview is this: The standard approach is to collect local client updates and aggregate them into a new global model, often using weighted averaging such as FedAvg. The weights may depend on local sample...

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How can model aggregation be achieved in Federated Learning?

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