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Paper Citation Record · LEDGER

Achieving Personalized Federated Learning with Sparse Local Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.11380.

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

pith.paper-citation-record.v1
2201.11380 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:01:27.581157Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T20:58:26.459809Z

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 d355e150-baee-41c2-aa03-99f720aefd57 · inbound

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey cites this paper.

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey Achieving Personalized Federated Learning with Sparse Local Models

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:58:26.462660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T20:58:16.237327Z digest=sha256:32ec377785d2b515db6ee6c4fb8a583781614d3f80a6e37cc7b3c1e390b3e80b

Observation 6509ea1d-d41d-4471-b2c6-44dd5ec7594c · inbound

Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity cites this paper.

Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity Achieving Personalized Federated Learning with Sparse Local Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T12:01:27.581157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:01:27.581157Z digest=sha256:d0c89b694373acaec57d6502f0d81593909e2f516bc917b40f19f4dfe08b5515