Pith. sign in

Paper Citation Record · LEDGER

Federated Meta-Learning with Fast Convergence and Efficient Communication

As of 6 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:c5c8239ad118ab21de71dc5667c7a30f74992bd98c9ab58fe23f18e1c2beadc3

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=sha256:232ee9a1f8089ffb7c9666f736e776f031a71858cfa44c59ff7726c08f523967

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=sha256:4b3f0cadefffe1e1be67c34e1bcaf2fd7e3d531ad02b19f78f77f191f9602e5d

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=sha256:901463e0ecfe6dbcefbf595b2a9005daefbf38a9df7733601930bcdeb70fbb53

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:cad1498751e963e04016063cf97101d67a8562b1aea4c91959f6f7c7a48de2d1

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:a62d2410a89425e2041a26fc6471e9c5a0669b2d1ed82e25240a5aff0fb35999