Paper Citation Record · LEDGER

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT

As of 16 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2502.06099.

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

pith.paper-citation-record.v1
2502.06099 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:49:23.376902Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy22
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f36446c2-669c-4d3c-bd03-ab3b1664ce70 · outbound

This paper cites Ahmad, R.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Ahmad, R

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.982925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4c29b919-c9e2-425f-ab46-134e3558fee1 · outbound

This paper cites A survey of recent advances in edge-computing-powered artificial intelligence of things,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT A survey of recent advances in edge-computing-powered artificial intelligence of things,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.970429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e2463263-5c0a-4f96-a14b-b57b116f28e0 · outbound

This paper cites Integrating connected vehicles in internet of things ecosystems: Challenges and solutions,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Integrating connected vehicles in internet of things ecosystems: Challenges and solutions,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.958248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0139d216-8f8a-4d31-9d06-5829d887d500 · outbound

This paper cites Research advances and chal- lenges of autonomous and connected ground vehicles,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Research advances and chal- lenges of autonomous and connected ground vehicles,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.946629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e5626f9c-1f4c-40a2-a7ca-fbdf0e70d99c · outbound

This paper cites V2x access technologies: Regulation, research, and remaining challenges,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT V2x access technologies: Regulation, research, and remaining challenges,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.934387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4b63de77-cf88-4eec-8105-6faa329406a7 · outbound

This paper cites Security and privacy issues in autonomous vehicles: A layer-based survey,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Security and privacy issues in autonomous vehicles: A layer-based survey,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.922021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8769044d-ee48-4429-9bd5-721bb499a945 · outbound

This paper cites Communication-efficient learning of deep networks from decentral- ized data,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Communication-efficient learning of deep networks from decentral- ized data,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T16:49:22.972363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eeceec5-02ad-4ccb-9b2e-34a044314739 · outbound

This paper cites Federated learning for connected and automated vehicles: A survey of existing approaches and challenges,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Federated learning for connected and automated vehicles: A survey of existing approaches and challenges,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.902915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0c7ca2e0-4679-4424-985a-a3d970666754 · outbound

This paper cites Hyperparameter tuning for federated learning– systems and practices,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Hyperparameter tuning for federated learning– systems and practices,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.892040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4ff95229-859b-476e-bec1-8db5ee359f01 · outbound

This paper cites Deep transfer learning based intrusion detection system for electric vehicular networks,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Deep transfer learning based intrusion detection system for electric vehicular networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.880176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8d2fb8ab-0989-419d-855d-29b55f1290e1 · outbound

This paper cites Deep learning- based intrusion detection systems: a systematic review,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Deep learning- based intrusion detection systems: a systematic review,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.866794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 71c7b348-275e-444d-a05b-69deccfb5efb · outbound

This paper cites Review of intrusion detection systems based on deep learning techniques: coher- ent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Review of intrusion detection systems based on deep learning techniques: coher- ent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.853161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 11c831e8-6991-4e69-ae92-d3311bbe6314 · outbound

This paper cites One-Shot Federated Learning.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT One-Shot Federated Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T16:49:23.171313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f958c70a-457e-48bb-974b-5f00149e64e1 · outbound

This paper cites A survey of federated learning for connected and automated vehicles,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT A survey of federated learning for connected and automated vehicles,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.838717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 38bda84c-2310-469c-a75f-a886c070c3f7 · outbound

This paper cites Privacy-preserving backdoor attacks mitigation in federated learning using functional encryption,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Privacy-preserving backdoor attacks mitigation in federated learning using functional encryption,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.826383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 739687fa-83b1-4883-8d54-f23f6db575ec · outbound

This paper cites Deepfed: Federated deep learning for intrusion detection in industrial cyber–physical systems,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Deepfed: Federated deep learning for intrusion detection in industrial cyber–physical systems,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.737224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f3632aef-9972-4b31-a167-308a5c648c06 · outbound

This paper cites Dïot: A federated self-learning anomaly detection system for iot,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Dïot: A federated self-learning anomaly detection system for iot,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.641159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 19fefbbd-998d-419a-b571-0e91a0eba83a · outbound

This paper cites Towards Federated Learning at Scale: System Design.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Towards Federated Learning at Scale: System Design

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T16:49:23.228986Z

Source-reported events for the cited work

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Observation 0926c307-2e17-47fc-bd36-b637063f0ae2 · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T16:49:23.273243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6837a74e-290c-4eca-837d-48c51598532f · outbound

This paper cites Resource-efficient machine learning in 2 kb ram for the internet of things,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Resource-efficient machine learning in 2 kb ram for the internet of things,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.629023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0843592e-137e-4747-bcc1-0cfdd114293a · outbound

This paper cites Novel deep learning-enabled lstm autoencoder architec- ture for discovering anomalous events from intelligent transportation systems,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Novel deep learning-enabled lstm autoencoder architec- ture for discovering anomalous events from intelligent transportation systems,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T16:49:23.346253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b6649b8-26af-4cba-9ac9-7dbdf58b3fd5 · outbound

This paper cites A review of deep transfer learning and recent advancements,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT A review of deep transfer learning and recent advancements,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.608767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3741563d-5f86-4b64-b260-45e24f4d4036 · outbound

This paper cites Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.596312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 422ef6d8-098b-424c-a934-8deab696f8ec · outbound

This paper cites Tinytl: Reduce memory, not parameters for efficient on-device learning,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Tinytl: Reduce memory, not parameters for efficient on-device learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.584721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eb2350c5-bf18-4172-8ddb-862d4f725c23 · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Flower: A Friendly Federated Learning Research Framework

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T16:49:23.364024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0fd0c4d-b3fd-48b9-bc5b-c94101b2ed3e · outbound

This paper cites A study on nsl-kdd dataset for intrusion detection system based on classification algorithms,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT A study on nsl-kdd dataset for intrusion detection system based on classification algorithms,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.572284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b10024c6-2f6f-45d5-a5a8-b88ca79a415a · outbound

This paper cites A detailed analysis of the kdd cup 99 data set,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT A detailed analysis of the kdd cup 99 data set,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.559505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7e540149-24ed-4670-89cf-f1738d121e95 · outbound

This paper cites Fl- ids: Federated learning-based intrusion detection system using edge devices for transportation iot,.

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT Fl- ids: Federated learning-based intrusion detection system using edge devices for transportation iot,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:49:23.502596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.