Paper Citation Record · LEDGER

Fine-Tuning Large Language Models with User-Level Differential Privacy

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2407.07737.

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

pith.paper-citation-record.v1
2407.07737 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:05:02.520377Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f4006fd1-d80f-413e-a2dd-f0258cdd653a · inbound

Scaling Laws for Differentially Private Language Models cites this paper.

Scaling Laws for Differentially Private Language Models Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T22:05:02.520377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 630895a8-50c6-4bbc-b73f-3489133076ea · inbound

Get Experience from Practice: LLM Agents with Record & Replay cites this paper.

Get Experience from Practice: LLM Agents with Record & Replay Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:22.165394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5650d8f2-58f9-4179-9bdc-ef1404651e15 · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:24.040736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation def44379-5fdb-4d66-9b89-b13190f6098d · inbound

On Design Principles for Private Adaptive Optimizers cites this paper.

On Design Principles for Private Adaptive Optimizers Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T21:09:31.221412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b994d25b-8049-4b31-8d14-3dc03894156e · inbound

On the Inherent Privacy of Zeroth Order Projected Gradient Descent cites this paper.

On the Inherent Privacy of Zeroth Order Projected Gradient Descent Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 2021

Resolution: malformed identifier; no resolver link, observed 2026-08-06T19:34:04.156388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c95b0279-7f4a-4e75-8419-044adfbac69a · inbound

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization cites this paper.

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T16:10:53.214154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ef2fd98-721b-49af-a167-94f0b1624143 · inbound

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey cites this paper.

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T15:26:33.422169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aecfc9ef-cd51-47b7-be06-4d5b6c698f74 · inbound

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning cites this paper.

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:30:31.793380Z

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.

Observation dd902fb5-26dd-4979-a27e-dc34690c9163 · inbound

PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees cites this paper.

PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-06-27T16:31:02.411009Z

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.

Observation 01a5efd4-37ab-419f-946d-6bd9c8ea213c · inbound

Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies cites this paper.

Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies Fine-Tuning Large Language Models with User-Level Differential Privacy

Reference 170

Resolution: unresolved; no resolver link, observed 2026-08-06T14:54:47.919996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.