Instruction: Discuss why client weighting is crucial in Federated Learning and explore strategies to optimize client contributions to the global model.
Context: This question assesses the candidate's understanding of client weighting mechanisms in Federated Learning and their ability to optimize these mechanisms for better model performance.
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The way I'd think about it is this: Client weighting matters because not all client updates should contribute equally. Sample count is the most common signal, but it is not the only one that matters. Data quality, recency, reliability, and trust can also...