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Paper Citation Record · LEDGER

Improving Accelerated Federated Learning with Compression and Importance Sampling

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.03240.

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

pith.paper-citation-record.v1
2306.03240 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 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:44:56.961994Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T14:39:16.542699Z

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 14025093-402e-4d22-bfca-c20462c0e79c · inbound

The Stochastic Multi-Proximal Method for Nonsmooth Optimization cites this paper.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Improving Accelerated Federated Learning with Compression and Importance Sampling

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T20:44:56.961994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:44:56.961994Z digest=sha256:37a9ed5d4c6c9637e3a1da168b616d7e83f3bd9a8b2d2260ed92936da1dfee8e

Observation 7a3bcec7-2b3e-4a9e-974a-551b5cd79bf5 · 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 Improving Accelerated Federated Learning with Compression and Importance Sampling

Reference 98

Resolution
verified exact
local_arxiv, observed 2026-08-15T14:39:16.546131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-15T14:39:14.542277Z digest=sha256:a6c83df57d114d4423661dfb1e30227ac956d88c163070d59ad8ca6a18ba4a08