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Paper Citation Record · LEDGER

Federated Meta-Learning with Fast Convergence and Efficient Communication

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1802.07876.

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

pith.paper-citation-record.v1
1802.07876 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:57:18.712993Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T06:55:10.648587Z

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 4bb30bd7-5842-4674-8bc5-baa029fcff43 · inbound

FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields cites this paper.

FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields Federated Meta-Learning with Fast Convergence and Efficient Communication

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T22:57:18.712993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:57:18.712993Z digest=sha256:edd416b309e0d26a1318092248013814063fc55a25e1dabd1a67d3082cdffdf1

Observation 5de9eafd-9e01-4b44-8705-9c755019e59f · inbound

Federated Learning with Heterogeneous and Private Label Sets cites this paper.

Federated Learning with Heterogeneous and Private Label Sets Federated Meta-Learning with Fast Convergence and Efficient Communication

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T16:18:09.987784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:18:09.987784Z digest=sha256:f1f7d45a791501665429c2dd29e04394dc958ecd2430b8b230fc00345eb4d3b5

Observation 97ff9de3-b924-4008-b2b2-65437e84c6d4 · inbound

When To Adapt? Adapting the Model or Data in Federated Medical Imaging cites this paper.

When To Adapt? Adapting the Model or Data in Federated Medical Imaging Federated Meta-Learning with Fast Convergence and Efficient Communication

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:11:06.028642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-09T20:32:49.268444Z digest=sha256:3d2886e53d672474d751b1d197624bbd783610eb9faaf19ac227eae0ec33158d

Observation ab169435-f71e-45b4-bafe-271e018fb8d9 · inbound

Personalized Digital Health Modeling with Adaptive Support Users cites this paper.

Personalized Digital Health Modeling with Adaptive Support Users Federated Meta-Learning with Fast Convergence and Efficient Communication

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-09T05:50:26.378565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-08T19:31:04.654667Z digest=sha256:d7d308a50cd37532e88072266649e11e048403bb49079c3bdc30d0193b5cac44

Observation 16264961-b47d-4bd5-877c-705bd78f0a0a · inbound

Personalized Digital Health Modeling with Adaptive Support Users cites this paper.

Personalized Digital Health Modeling with Adaptive Support Users Federated Meta-Learning with Fast Convergence and Efficient Communication

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:55:10.651952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-15T06:52:32.508141Z digest=sha256:2fa7ce8aa87f3edce02a7ad217c004fa0cbec000d17582be349af0c08b107a5c

Observation f13ddd3e-cdd6-4c28-b4bf-e59a042ef38d · inbound

Joint Channel Estimation and Dynamics-Aware Grouping for Time-Varying RIS-Assisted OTA Federated Learning cites this paper.

Joint Channel Estimation and Dynamics-Aware Grouping for Time-Varying RIS-Assisted OTA Federated Learning Federated Meta-Learning with Fast Convergence and Efficient Communication

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-01T18:53:34.726901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:53:34.726901Z digest=sha256:66e075073e06151d63b35dff6547ca889991bed86651c7ee5747b6e5593b2af3