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Paper Citation Record · LEDGER

Federated Learning on Riemannian Manifolds with Differential Privacy

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.10029.

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

pith.paper-citation-record.v1
2404.10029 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:24:43.219809Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T20:11:11.187116Z

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 1b0480a8-0610-495e-b5ca-8a16d61ecb1d · inbound

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach cites this paper.

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach Federated Learning on Riemannian Manifolds with Differential Privacy

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T11:24:43.219809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:24:43.219809Z digest=sha256:02c612ab29dc6ec5bee482951a4d6b6ef22ded6d5f0d5793350750b493700642

Observation 2ea04f0a-af57-4d09-8d7d-30e9d1ed8e8d · inbound

FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet cites this paper.

FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet Federated Learning on Riemannian Manifolds with Differential Privacy

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:11:11.190876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T10:03:44.180537Z digest=sha256:9bbbe3c8a18f6ba67e9f93c6e248cc683c960d01c583480fc4805d5fb32a2c13