Paper Citation Record · LEDGER

Inverting Gradients -- How easy is it to break privacy in federated learning?

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2003.14053.

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

pith.paper-citation-record.v1
2003.14053 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 2026-08-18T06:34:40.430872+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:30:12.730708Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:09:44.313949Z

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 94019469-b930-430e-91cc-3ce0b17479a4 · inbound

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation cites this paper.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation Inverting Gradients -- How easy is it to break privacy in federated learning?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T12:49:37.522830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 888d612c-4034-4378-acff-53e86a41aa99 · inbound

BlindFL: Segmented Federated Learning with Fully Homomorphic Encryption cites this paper.

BlindFL: Segmented Federated Learning with Fully Homomorphic Encryption Inverting Gradients -- How easy is it to break privacy in federated learning?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T18:06:32.236774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a9b9164-61de-4beb-8451-618c0f915957 · inbound

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks cites this paper.

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks Inverting Gradients -- How easy is it to break privacy in federated learning?

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T12:30:12.730708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3d1e28b-f381-426e-a2ad-2d47ce7a099b · inbound

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures cites this paper.

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures Inverting Gradients -- How easy is it to break privacy in federated learning?

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T04:28:00.361214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abe4a983-2244-486f-ac99-b736b66075d9 · inbound

Exposing the Illusion of Erasure in Knowledge Editing for LLMs cites this paper.

Exposing the Illusion of Erasure in Knowledge Editing for LLMs Inverting Gradients -- How easy is it to break privacy in federated learning?

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:09:44.316443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 363e6b18-501c-47ef-acd7-63c613010722 · inbound

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks cites this paper.

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks Inverting Gradients -- How easy is it to break privacy in federated learning?

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-02T02:54:04.056448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d23328f3-20e6-4a3c-a5a3-b9d36851d6a5 · inbound

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement cites this paper.

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement Inverting Gradients -- How easy is it to break privacy in federated learning?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T22:47:13.239611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 835afc7b-6c25-4b9a-8ea9-2bb57a03bbf5 · inbound

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement cites this paper.

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T01:42:09.708146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21b60cd9-a31d-47c3-b3f5-a7ffe8aef171 · inbound

Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation cites this paper.

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T15:20:19.537986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.