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Paper Citation Record · LEDGER

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats

As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2505.23706.

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

pith.paper-citation-record.v1
2505.23706 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:42:55.772098Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

  • verified exact2
  • verified fuzzy2
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 52f2e2b6-6665-419a-919b-3210eb8cc97c · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:58.290525Z

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.

source=pdf_text observed=2026-08-07T12:42:54.765769Z digest=sha256:b736877c69dbaac418c721fefeebceec5e02fd2f7ddb0f67d0c500ac65df2152

Observation cff9b789-158b-4de3-8809-993b00d2b0ad · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:58.179328Z

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.

source=pdf_text observed=2026-08-07T12:42:54.931645Z digest=sha256:c5b0db73442d211554bfe5ae872263d6f3649fadb1a84a072e287968a9fadf38

Observation 6bed0f75-4862-4958-93b1-d60f7991296b · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:58.136848Z

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.

source=pdf_text observed=2026-08-07T12:42:55.008636Z digest=sha256:5b887c2392f8f6930e1f73fda9796b1a40b2d02e5cc048769e79637cd23ae7ca

Observation 74fbe3f2-8ac0-4556-a423-9f0a99fd78f2 · outbound

This paper cites van der Hei, Arnaud Kaiser, Pascal Urien, and Frank Kargl.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats van der Hei, Arnaud Kaiser, Pascal Urien, and Frank Kargl

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:42:57.985560Z

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.

source=pdf_text observed=2026-08-07T12:42:55.062945Z digest=sha256:df77f6ec79ee1445cced1e9e80becb7c04ae9ece365a021d7cc5e35e6c1af952

Observation 93c0ec5a-9885-4f8f-b17d-f86d62a244c6 · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:57.904368Z

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.

source=pdf_text observed=2026-08-07T12:42:55.108623Z digest=sha256:82626e5c9eac726e369d8022106808a1147bddabe57de7fcc496043538427442

Observation 0a3f2088-0693-4000-a877-3d5eab8eb6c6 · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:57.665793Z

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.

source=pdf_text observed=2026-08-07T12:42:55.158495Z digest=sha256:7ca82c9af434f7680d2494d0603aa90bb2913b4e152716e6c140feda150a628b

Observation c3cc38cf-328f-49ce-9ecf-549a7557dc39 · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:57.510941Z

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.

source=pdf_text observed=2026-08-07T12:42:55.273237Z digest=sha256:0ed9f06f8d480809f049889a9fc6c58deb026e0f79fb9d47e5b1ab3e44ec3cdf

Observation 784ea136-a699-42b5-bbc1-9848ab3a01b1 · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:57.268923Z

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.

source=pdf_text observed=2026-08-07T12:42:55.325174Z digest=sha256:2e6bcc91beae51392a3a89bb4776f9d31b8651083c56b8d00ecd76b1846f7572

Observation 03e6b520-db7c-47e8-8dc9-ca3bd0324228 · outbound

This paper cites Jamming Attacks on Federated Learning in Wireless Networks.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Jamming Attacks on Federated Learning in Wireless Networks

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:42:56.274386Z

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.

source=pdf_text observed=2026-08-07T12:42:55.399024Z digest=sha256:1e265c74f50ccd7bd3af8ee72b400a02900eccabba01d4967a3587d202cb16f4

Observation 413d2ccc-a07b-40d0-ac64-12ee29416404 · outbound

This paper cites Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:42:56.002160Z

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.

source=pdf_text observed=2026-08-07T12:42:55.449144Z digest=sha256:1819c214ca283250430baf5cb32f97b1c1c8c52b69f276bf86e6f6f4d63266d6

Observation 49bb25c7-6eac-460e-bee4-263cfe053912 · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:57.096817Z

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.

source=pdf_text observed=2026-08-07T12:42:55.509795Z digest=sha256:6e3b6a147e6aa23ecab817c2e21cc3abf2768b2a1a4acbd15e930b8a00c1beb6

Observation 08646862-430e-4b10-8b62-0a75884f2c55 · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:57.017924Z

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.

source=pdf_text observed=2026-08-07T12:42:55.568962Z digest=sha256:1b1e6b10ee2164785718af1c1d6bc2455cf44f54157dc940f3c7f552d21c02be

Observation 429b5173-e4dc-4fa0-8acf-053d05e56570 · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:56.845249Z

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.

source=pdf_text observed=2026-08-07T12:42:55.673831Z digest=sha256:13bf47098515c14b83fdad69be70ecabc93d5161eadf5197c03e5e6d73123794

Observation c6141356-88e7-4062-b9bd-a43a713b1a74 · outbound

This paper cites an unresolved cited work.

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:42:56.544396Z

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.

source=pdf_text observed=2026-08-07T12:42:55.772098Z digest=sha256:568e22eb4c83843669b8f3f499c38005fb1aa9d95b4257bf97123f05812008a9

Observation 457ba90e-6b9f-408f-a0d0-446f189e5219 · outbound

This paper cites Journal on Autonomous Transportation Systems 2, 3 (2024).

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats Journal on Autonomous Transportation Systems 2, 3 (2024)

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:42:58.228385Z

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.

source=pdf_text observed=2026-08-07T12:42:54.838263Z digest=sha256:5533e942428428034c0491d97aa0664cea31300fd42855d69ea8d9fb6c029c33

Pith citing papers

No inbound Pith citation observations are available.