Instruction: Create a strategy for dynamic model aggregation that adapts to changing data distributions in Federated Learning.
Context: Candidates are expected to propose innovative aggregation strategies that can dynamically adjust to non-IID data, ensuring effective learning.
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The way I'd approach it in an interview is this: A dynamic aggregation strategy should adapt weights based on more than sample count. I would consider update quality, staleness, trust signals, domain relevance, and whether certain clients are consistently outliers or underrepresented.
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