These sources collectively discuss advancements in **scalable, efficient, and secure machine learning (ML) data systems**, often within the context of large-scale datacenter deployments. Several papers address the performance and security trade-offs of using **Confidential Computing (CC)** and **Trusted Execution Environments (TEEs)** for large language models (LLMs) and database systems, including utilizing technologies like Intel TDX and specialized frameworks for FPGAs. Other documents focus on optimizing the **ML training data pipeline**, detailing systems like **RecD** for deduplication in deep learning recommendation models (DLRMs) to improve efficiency and **cedar**, a framework for automated pipeline optimization that addresses bottlenecks in data preprocessing, caching, and operator reordering. Finally, one source introduces **MinionS**, a collaboration protocol between small on-device LMs and frontier cloud LMs designed to significantly reduce remote inference costs while maintaining high performance for data-intensive reasoning tasks.Sources:https://arxiv.org/pdf/2505.16501https://arxiv.org/pdf/2502.15964https://hazyresearch.stanford.edu/blog/2025-05-12-securityhttps://arxiv.org/html/2411.03357v1https://purl.stanford.edu/dm268wp3942https://stacks.stanford.edu/file/dm268wp3942/mark_zhao_dissertation-augmented.pdfhttps://arxiv.org/pdf/2502.11347

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