Explain the role of a local model in Federated Learning.

Instruction: Describe the function and importance of a local model within the Federated Learning framework.

Context: This question aims to evaluate the candidate's grasp on how Federated Learning leverages local models for decentralized training, emphasizing the significance of local computations in enhancing privacy and reducing communication overhead.

Example Answer

The way I'd explain it in an interview is this: The local model is the client-side copy of the global model that gets updated using local data before sending an update back for aggregation. It is the mechanism that lets learning happen where the data lives.

Its role is more than computation. It also defines how much adaptation each client contributes, how stale updates can become, and how privacy and personalization interact. In some systems, the local model is also partly personalized rather than being just a temporary training replica.

What matters in an interview is not only knowing the definition, but being able to connect it back to how it changes modeling, evaluation, or deployment decisions in practice.

Common Poor Answer

A weak answer says the local model trains on the device, without explaining its role in update quality, personalization, and aggregation behavior.

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