AIBearisharXiv – CS AI · 7h ago7/10
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Silent Failures in Federated Personalization of Foundation Models
Researchers identify 'Silent Failures'—undetectable trustworthiness issues like bias amplification and alignment erosion—that emerge when foundation models are personalized via federated learning under privacy constraints. The structural gap between federated system benchmarks and centralized behavioral tests creates blind spots in model safety monitoring, raising concerns for regulated AI deployment.