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Paper Citation Record · LEDGER

Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2004.08546.

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

pith.paper-citation-record.v1
2004.08546 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:27:40.460009Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:38:55.117417Z

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 5c9f1dde-b0d2-45a2-9e50-4d6b82574aa8 · inbound

DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Training cites this paper.

DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Training Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-16T12:07:50.753546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-16T12:05:06.713841Z digest=sha256:35b5ff2d8935b65bfc1d75a20e27d90b84c81505c3dff2c26d5ca9e853d45972

Observation ba4013fb-55be-4110-a0ed-707ca4d11f07 · inbound

Auto-FL-Research: Agentic Search for Federated Learning Algorithms cites this paper.

Auto-FL-Research: Agentic Search for Federated Learning Algorithms Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-07-03T20:38:55.119143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-07-03T20:33:30.815266Z digest=sha256:56a37ac854af74ac664a3ef9591c8886ce81c5fa7c6cb7e8d0b443bf6931e8ba

Observation 442af116-77ae-4314-ba93-685304953782 · inbound

FedJigsaw: Multi-Agent Collaborative Model Reassembly for Decentralized Heterogeneous Federated Learning cites this paper.

FedJigsaw: Multi-Agent Collaborative Model Reassembly for Decentralized Heterogeneous Federated Learning Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-04T19:27:40.460009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T19:27:40.460009Z digest=sha256:c68d43ed29a60b46efa8ae621533de798272299b5b91718803aa0226a19b5114