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User feedback prediction can be learned, revealing critical performance gaps in AI assistants that traditional evaluation methods overlook.
Federated learning struggles when data quality varies across clients, but FedQual solves this with a novel approach that calibrates low-quality clients while preserving high-quality autonomy.
