The quest to build robust medical large language models (LLMs) is trapped in a fundamental conflict: these models require vast, diverse datasets to be clinically useful, but patient data is rightly locked down in siloed hospital systems, protected by stringent regulations like HIPAA. Containerized federated learning is emerging as the architectural breakthrough that resolves this dilemma. This approach packages a model’s training algorithm into secure, ephemeral containers that are dispatched to each hospital’s private cloud—where they learn from local patient data without any Protected Health Information (PII) ever leaving the source. Only the anonymous, aggregated model updates are sent back for consolidation, creating a powerful, globally-informed model that has never directly seen an individual’s private records. This video explores how this “move the algorithm, not the data” paradigm is enabling unprecedented collaboration across health networks, turning isolated data silos into a collective force for medical discovery while maintaining an ironclad commitment to patient privacy.
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