Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Inverting Gradients -- How easy is it to break privacy in federated learning?
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2003.14053.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T12:49:37.522830Z
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 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 2026-08-14T06:32:32.682623+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.
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 2026-08-03T01:42:09.708146Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.