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Paper Citation Record · LEDGER

Adversarial training in communication constrained federated learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2103.01319.

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

pith.paper-citation-record.v1
2103.01319 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:15:41.034861Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T11:18:44.701515Z

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 c4c4180d-09ea-44e2-bd61-f8e563175426 · inbound

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach cites this paper.

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach Adversarial training in communication constrained federated learning

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-05T22:15:41.034861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:15:41.034861Z digest=sha256:91896eb6e2c2153e2b1976810599e29b0ebd53526d0f9292d0bbf8466e32ffe1

Observation 2594e414-0630-4514-945d-5b8e95d43574 · inbound

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models cites this paper.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Adversarial training in communication constrained federated learning

Reference 2019

Resolution
verified exact
local_arxiv, observed 2026-08-05T11:18:44.797116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T11:18:43.415382Z digest=sha256:a8972be9e475af39606cf98afffb0fb691bdefc3ca18329faa8c4c6d7231a9c0