Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.10375.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:03:19.294443Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T17:38:43.802362Z
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 ad8c6798-2809-4310-afa8-0ddc679ffcd9 · inbound
Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation cites this paper.
Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T04:42:35.078871Z
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 342a4049-82af-4b8b-9d28-04da4bae73f7 · inbound
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions cites this paper.
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
Reference 115
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:03:19.294443Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1fc85b38-acb0-4778-9199-f5ea395dd297 · inbound
Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs cites this paper.
Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
Reference 75
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:51:54.931467Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91bba755-e862-4223-a610-7e945467eeeb · inbound
Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains cites this paper.
Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
Reference 51
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T05:48:04.475662Z
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 4af6225d-e493-448e-af10-438b02890f23 · inbound
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs cites this paper.
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities
Reference 75
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T17:38:43.804000Z
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.