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Paper Citation Record · LEDGER

Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2404.06448.

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

pith.paper-citation-record.v1
2404.06448 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:32:23.285294Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T18:41:09.141387Z

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 5464dfd2-0b43-4c5d-b850-9766d2c0fa30 · inbound

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations cites this paper.

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-09T00:50:00.012742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:50:00.012742Z digest=sha256:516af3d63b0227d332040c8f239629c1782226bf1715ecfcb991164797eb2aa3

Observation 02289aab-2c12-4a1a-bde4-d2b5360162cd · inbound

A Contemporary Survey of Large Language Model Assisted Program Analysis cites this paper.

A Contemporary Survey of Large Language Model Assisted Program Analysis Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-09T05:32:23.285294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T05:32:23.285294Z digest=sha256:abd4af472ba72a91d49072adb03cf6e95d76a2e36b57233f7d509682a96b69ce

Observation 006ef0b5-57c1-47a2-8eec-c8faca2d4ff2 · inbound

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems cites this paper.

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T05:19:13.077585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:19:13.077585Z digest=sha256:93bb2b40c00d06761ad4119a6d87cb8df5a048d4c453cf0bcf525e201340b700

Observation 64471ad8-5c5f-48f8-8480-c70048f33c74 · inbound

Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generation cites this paper.

Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generation Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T04:28:29.204819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:28:29.204819Z digest=sha256:acbf3045f00561c629644db695691714ccebcb29e667a6f18b83e9dfea569018

Observation 16ad7491-df97-4212-b036-8887fe027ada · inbound

A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation cites this paper.

A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T19:24:41.857105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:24:41.857105Z digest=sha256:dc942e43845f7e663d92d9e640ea48e7fae64ab67374179f3fd07611a935a729

Observation 5509b089-c443-4ed9-b5a3-c8cfe5058654 · inbound

PHandover: Parallel Handover in Mobile Satellite Network cites this paper.

PHandover: Parallel Handover in Mobile Satellite Network Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T18:45:38.797840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:45:38.797840Z digest=sha256:e13553be7942aa8f222d8da92e23580d221c5dd09f113da62e9c33493dee947a

Observation f96b75d9-a429-4e46-93b0-5db02dfa15e0 · inbound

Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring cites this paper.

Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-05T21:14:36.290338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T21:14:36.290338Z digest=sha256:5c39bc85d1b1ef7c53f1375b2ca96f429f7ce1e7100ba32c246f79b5754032fc

Observation cd58f7a6-7d79-41a2-bd6e-166b9a9e903a · inbound

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge cites this paper.

FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T16:22:25.418475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:22:25.418475Z digest=sha256:19b5259dc38a521301a9b93d483d7564a8e7896954c67e0ea7b5a7289ba720e9

Observation f5a97e80-3aed-4033-a74d-85bfdafe000d · inbound

Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning cites this paper.

Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:41:09.155341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T14:50:12.857642Z digest=sha256:ad5caadb8d60e7617a8a224618082ee960132223ced7b45045cf9871a370e7fd