Paper Citation Record · LEDGER

Federated Learning Meets Natural Language Processing: A Survey

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2107.12603.

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

pith.paper-citation-record.v1
2107.12603 v1

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 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:47:35.340554Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T09:36:26.289960Z

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 1cf1ff8a-1245-49e5-811d-5663c8593e7c · inbound

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection cites this paper.

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection Federated Learning Meets Natural Language Processing: A Survey

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:52.567281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d072e635-a15e-4167-973f-4ed594d51b58 · inbound

Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission cites this paper.

Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission Federated Learning Meets Natural Language Processing: A Survey

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:47.296289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65367e42-7881-4543-a76b-033889952a9b · inbound

Efficient Federated Learning with Timely Update Dissemination cites this paper.

Efficient Federated Learning with Timely Update Dissemination Federated Learning Meets Natural Language Processing: A Survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:20:25.104909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96ddeb3d-0ea2-4cde-b474-b68f57f3607a · inbound

Adaptive Federated Distillation for Multi-Domain Non-IID Textual Data cites this paper.

Adaptive Federated Distillation for Multi-Domain Non-IID Textual Data Federated Learning Meets Natural Language Processing: A Survey

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T16:47:35.340554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce0f4b06-cf12-4867-8d94-c3376e66c72b · inbound

AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning cites this paper.

AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning Federated Learning Meets Natural Language Processing: A Survey

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:36:26.291841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6f818c32-ef8b-4aae-9e0c-3dbd7a5bade0 · inbound

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization cites this paper.

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization Federated Learning Meets Natural Language Processing: A Survey

Reference 161

Resolution: unresolved; no resolver link, observed 2026-08-15T14:39:14.737170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.