Paper Citation Record · LEDGER

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles

As of 13 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2411.13979.

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

pith.paper-citation-record.v1
2411.13979 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:46:43.501831Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

16 of 16 outbound references displayed

verified exact0
verified fuzzy3
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e52c6af1-afd6-486f-a999-f1303bc31e12 · outbound

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

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Communication-efficient learning of deep networks from decentralized data,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.433794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db5270d5-f1b6-4512-8401-2958fbeecac9 · outbound

This paper cites Personalized Federated Learning: A Meta-Learning Approach.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Personalized Federated Learning: A Meta-Learning Approach

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.438985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 656ce355-7424-48f6-aecf-03aea97a07c6 · outbound

This paper cites Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.443874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9af7ae8-6f7b-47d0-b028-34109b61105f · outbound

This paper cites Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:46:43.674018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c472fb31-4061-4263-8535-7a7dde29d529 · outbound

This paper cites Federated Learning with Non-IID Data.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Federated Learning with Non-IID Data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.453722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fced50a-c8f0-46ef-bcb7-0189265746f0 · outbound

This paper cites Federated optimization in heterogeneous networks,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Federated optimization in heterogeneous networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.458448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b84a086-0ca5-463c-8f33-5b2545b12697 · outbound

This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.463379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3716ebdc-8c27-4d3c-94a2-23e3a5c75ecb · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles The cityscapes dataset for semantic urban scene understanding,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.467598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9128489a-69fd-4801-80e9-d8a55cedff8e · outbound

This paper cites Least squares quantization in pcm,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Least squares quantization in pcm,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.471696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddb0a3e8-9aaa-4955-98c2-6bf146046c09 · outbound

This paper cites K-means++ the advantages of careful seeding,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles K-means++ the advantages of careful seeding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:46:43.625811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5fe74464-0312-413b-8c07-d0556a261476 · outbound

This paper cites Personalized federated learning using hypernetworks,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Personalized federated learning using hypernetworks,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.480812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35428ddb-a772-48c0-b056-47305d3e429b · outbound

This paper cites Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.484847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 137037ea-82db-47e3-bec2-a21ef7598306 · outbound

This paper cites Mio-tcd: A new benchmark dataset for vehicle classification and localization,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Mio-tcd: A new benchmark dataset for vehicle classification and localization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:46:43.594549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 07fca3c3-e70c-42fa-98a6-b77260317ca0 · outbound

This paper cites Backpropagation applied to handwritten zip code recognition,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Backpropagation applied to handwritten zip code recognition,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.493385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5fe6a39-0da1-4ac9-a205-77db2ddeeda7 · outbound

This paper cites Deep residual learning for image recognition,.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Deep residual learning for image recognition,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.497413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05380936-1464-49ab-85d3-9c5cac117e80 · outbound

This paper cites Think Locally, Act Globally: Federated Learning with Local and Global Representations.

FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles Think Locally, Act Globally: Federated Learning with Local and Global Representations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T15:46:43.501831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.