Paper Citation Record · LEDGER

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.15080.

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

pith.paper-citation-record.v1
2310.15080 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:46:23.943249Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T11:32:36.824013Z

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 ccb837de-d0d5-44d3-8ad5-957164b11df2 · inbound

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-05-13T11:32:36.825821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f2f428d0-19de-4fd6-9d62-d03fec2a3f1b · inbound

When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair cites this paper.

When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T04:46:23.943249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8db3b7a-3086-49d5-97b1-2ac9ec155c59 · inbound

FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems cites this paper.

FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T10:29:00.834353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dbe40fa-1c77-43eb-8d62-62ff97fd82d2 · inbound

FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models cites this paper.

FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:29.106406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 156d8865-31d3-40d2-b415-a212815bd995 · inbound

FedNAMs: Performing Interpretability Analysis in Federated Learning Context cites this paper.

FedNAMs: Performing Interpretability Analysis in Federated Learning Context Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:33:37.443353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d0c1700-4809-4fb7-8717-5c24d9f0c751 · inbound

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models cites this paper.

FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-05T11:18:42.286695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.