Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2004.08546.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T15:38:09.396659Z
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 e9d2db3f-1427-4794-8b51-eec3ecec1aec · inbound
AutoML: A Survey of the State-of-the-Art cites this paper.
AutoML: A Survey of the State-of-the-Art Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search
Reference 279
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:38:09.396659Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 82bebcd6-e180-4c44-b4b1-7d00775441a1 · inbound
Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift cites this paper.
Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:46:08.278291Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-14T06:32:32.682623+00:00.
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 2026-08-14T06:32:32.682623+00:00.
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.