Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.19389.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:45:38.916593Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-17T01:53:51.024613Z
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 07d8a41c-8e3d-452b-95ef-9bfe155db3ad · inbound
FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures cites this paper.
FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:45:38.916593Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a81e1337-b689-44c7-a144-32aed615329d · inbound
Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration cites this paper.
Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T01:53:51.026768Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.