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Paper Citation Record · LEDGER

On Model Protection in Federated Learning against Eavesdropping Attacks

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2504.02114.

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

pith.paper-citation-record.v1
2504.02114 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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:01:28.926113Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T19:29:47.982076Z

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 fbaf3483-5a5d-4828-8671-a0c9bb67fc29 · inbound

ModShift: Model Privacy via Designed Shifts cites this paper.

ModShift: Model Privacy via Designed Shifts On Model Protection in Federated Learning against Eavesdropping Attacks

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-15T18:01:28.926113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.926113Z digest=sha256:7c507da1fef0fb4295a6c0c3dc88e1904199924f0ebb8e51c22efe770c32ea9d

Observation 456829d1-b446-4b04-bbc1-57aa3785848a · inbound

MaxModShift: Model Privacy via Designed Shifts cites this paper.

MaxModShift: Model Privacy via Designed Shifts On Model Protection in Federated Learning against Eavesdropping Attacks

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-11T19:29:47.988201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-11T19:29:47.911886Z digest=sha256:a935bad0236e4b52d41d4a3a2b1f2ae8d0ed7736d9cf4ed7eed376721b6c6902