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Paper Citation Record · LEDGER

FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.14579.

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

pith.paper-citation-record.v1
2310.14579 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:24:24.489701Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T20:10:18.193283Z

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 276f0d2b-1d60-4383-8c60-ae7606859838 · inbound

The Impact of Cut Layer Selection in Split Federated Learning cites this paper.

The Impact of Cut Layer Selection in Split Federated Learning FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T11:24:24.489701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:24.489701Z digest=sha256:1b8ceb48029ebc194e8b347a5d3c941b44e9f63c3a7e90f8d369ca211fd35a2c

Observation 02f6f34a-16fb-46b3-9623-5cb0aa60c2e8 · inbound

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models cites this paper.

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-15T20:10:18.195855Z

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.

source=pdf_text observed=2026-05-15T20:07:38.811639Z digest=sha256:3bac3782d3aa66374f8282680f962a87b57db92cda80a9c1e17fd5cb3d13c48e