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  "Title": "Privacy-Preserving Distributed Algorithms",
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  "Date": "2025-11-11",
  "Authors@R": "c(person(\"Chongliang\", \"Luo\", role=c(\"cre\"), email =\"luocl3009@gmail.com\"),\nperson(\"Rui\", \"Duan\", role=\"aut\"), person(\"Mackenzie\", \"Edmondson\", role=\"aut\"),\nperson(\"Jiayi\", \"Tong\", role=\"aut\"), person(\"Xiaokang\", \"Liu\", role=\"aut\"),\nperson(\"Kenneth\", \"Locke\", role=\"aut\"),\nperson(\"Jie\", \"Hu\", role=\"aut\"),\nperson(\"Bingyu\", \"Zhang\", role=\"aut\"),\nperson(\"Yicheng\", \"Shen\", role=\"aut\"),\nperson(\"Yudong\", \"Wang\", role=\"aut\"),\nperson(\"Yiwen\", \"Lu\", role=\"aut\"),\nperson(\"Lu\", \"Li\", role=\"aut\"),\nperson(\"Yong\", \"Chen\", role=\"aut\", email =\"ychen123@upenn.edu\"),\nperson(\"Penn Computing Inference Learning (PennCIL) lab\", role = c(\"cph\")))",
  "Description": "A collection of privacy-preserving distributed algorithms\n(PDAs) for conducting federated statistical learning across\nmultiple data sites. The PDA framework includes models for\nvarious tasks such as regression, trial emulation, causal\ninference, design-specific analysis, and clustering. The PDA\nalgorithms run on a lead site and only require summary\nstatistics from collaborating sites, with one or few\niterations. The package can be used together with the online\ndata transfer system (<https://pda-ota.pdamethods.org/>) for\nsafe and convenient collaboration. For more information, please\nvisit our software websites: <https://github.com/Penncil/pda>,\nand <https://pdamethods.org/>.",
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