Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.04301.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:58:18.065197Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T07:16:02.289583Z
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 bd58f5c1-3156-4a56-b6ff-589aa01630ec · inbound
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise cites this paper.
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:58:18.065197Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 983b968d-709c-43ad-8fca-a87fd6835de7 · inbound
Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise cites this paper.
Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T07:16:02.295979Z
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.