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Paper Citation Record · LEDGER

Efficient Deep Learning on Multi-Source Private Data

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1807.06689.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1807.06689 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:58:41.505241Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-11T12:40:58.915872Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 55255262-2905-4a3a-a5a1-f0ebf11e3418 · inbound

Protecting Confidentiality, Privacy and Integrity in Collaborative Learning cites this paper.

Protecting Confidentiality, Privacy and Integrity in Collaborative Learning Efficient Deep Learning on Multi-Source Private Data

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-11T17:58:41.505241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:58:41.505241Z digest=sha256:d92bd15c9385a41588bc4ceff1ed152dde3b413382e794be2439b6b755705ca1

Observation 2c68c854-0ef6-4bb1-93a5-7d19345ea9be · inbound

Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey cites this paper.

Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey Efficient Deep Learning on Multi-Source Private Data

Reference 69

Resolution
verified exact
local_arxiv, observed 2026-08-11T12:40:58.920317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T12:40:57.091319Z digest=sha256:559937f14e224cbd5809b3a181429ce0cd5f69ea19eae3dbd637595518b96846