Instruction: Discuss the impact of learning rate on the performance of a Federated Learning model and how it can be optimized.
Context: Aimed at evaluating the candidate's knowledge on the importance of learning rate in the context of Federated Learning, including its effects on model convergence and the strategies for its optimization to enhance model performance.
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The way I'd explain it in an interview is this: Learning rate is especially important in federated learning because local updates are applied on heterogeneous data before being aggregated globally. If the learning rate is too high, local models can drift too far apart...
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