SENTRA:Privacy-Preserving Training in Outsourced Cloud Environments

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Training machine learning models in untrusted clouds requires strong guarantees of confidentiality, integrity, and correctness, while remaining scalable and resilient to node churn. These challenges are further amplified in emerging agentic AI systems, where autonomous and distributed learning components require trustworthy coordination and secure state management across heterogeneous cloud environments. Existing Trusted Execution Environments (TEEs) lack scalability and remain vulnerable to sid