pdaSync

update pda control if ready (run by lead)

A collection of privacy-preserving distributed algorithms (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.

Chongliang Luo

Privacy-Preserving Distributed Algorithms

Rui Duan

Mackenzie Edmondson

Jiayi Tong

Xiaokang Liu

Kenneth Locke

Jie Hu

Bingyu Zhang

Yicheng Shen

Yudong Wang

Yiwen Lu

Lu Li

Yong Chen

Penn Computing Inference Learning (PennCIL) lab 

pdaSync function

<dl><dt>config</dt>
<dd>cloud configuration</dd>
<dt>upload_without_confirm</dt>
<dd>logical. TRUE if want silent upload, no interactive confirm</dd>
<dt>silent_message</dt>
<dd>logical. TRUE if want to mute message</dd>
<dt>digits</dt>
<dd>digits after decimal points in the output json files</dd></dl>

Arguments

pda control synchronize — pdaSync

<dl>

<dt>config</dt>
<dd>cloud configuration</dd>


<dt>upload_without_confirm</dt>
<dd>logical. TRUE if want silent upload, no interactive confirm</dd>


<dt>silent_message</dt>
<dd>logical. TRUE if want to mute message</dd>


<dt>digits</dt>
<dd>digits after decimal points in the output json files</dd>

</dl>

pda control synchronize

pdaSync: pda control synchronize

Description

Usage

Value

Arguments

See Also