Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.02912.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:22:08.735921Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T21:27:05.957346Z
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 9711b3bd-2ceb-489d-abdc-c6814a4659a1 · inbound
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI cites this paper.
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:22:08.735921Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7bb86e9d-62bb-49fc-bc34-853a12dacb3d · inbound
Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models cites this paper.
Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:47:30.547245Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3103031-0804-4672-aeee-e4d4bb02ee83 · inbound
PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction cites this paper.
PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:27:06.010515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.