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Paper Citation Record · LEDGER

FAT: Federated Adversarial Training

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2012.01791.

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

pith.paper-citation-record.v1
2012.01791 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:06:13.118900Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:15:41.161423Z

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 b04ebc99-aad5-4aaa-817f-57c1e7e2378a · inbound

How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning? cites this paper.

How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning? FAT: Federated Adversarial Training

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-11T18:06:13.118900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T18:06:13.118900Z digest=sha256:e640f8caa966c56970704c2daf82ae62a5a8b407ae829a3b9410e650d138d516

Observation ba126d7e-cf51-407a-8e1b-7d3833bf6417 · 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 FAT: Federated Adversarial Training

Reference 88

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:15:41.166251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-05T22:15:41.134392Z digest=sha256:5214b9818cf90b7798f28e19c80a0f8e38b414ca1b65e5b592015dce55d6a077