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FedCC enables clients to handle ambiguity in data, leading to a remarkable 67.3% accuracy even when facing severe label distribution skews.
SLMs can match the reasoning performance of much larger models by simply re-ranking their own top-K token predictions, eliminating the need for expensive LLM calls at inference time.