Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.17267.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:16:30.662916Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T19:08:54.376595Z
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 6b331ac5-1f28-43f1-900a-ec6e37c607ea · inbound
A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning cites this paper.
A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:16:30.662916Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d3ae7dd-673c-4f3c-a5f0-d5f1ecbf6ed8 · inbound
UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models cites this paper.
UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:08:54.378054Z
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.