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Paper Citation Record · LEDGER

FedFQ: Federated Learning with Fine-Grained Quantization

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.08977.

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

pith.paper-citation-record.v1
2408.08977 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:49:42.534162Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:11:00.305015Z

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 489e08a5-8667-480f-899f-cbd2c4cd0c96 · inbound

FedHQ: Hybrid Runtime Quantization for Federated Learning cites this paper.

FedHQ: Hybrid Runtime Quantization for Federated Learning FedFQ: Federated Learning with Fine-Grained Quantization

Reference 2009

Resolution
unresolved
no resolver link, observed 2026-08-15T20:49:42.534162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:49:42.534162Z digest=sha256:5c4b52ed2de1d8792c4288d90df1c7819a7ebfc167d5f091c242ec16b035fbbb

Observation c178f8d0-8b2d-4c59-bb91-0b96b450a43e · inbound

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning cites this paper.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning FedFQ: Federated Learning with Fine-Grained Quantization

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:11:00.389558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T20:10:56.393617Z digest=sha256:250ea5d27d3146593005cdbd11909b746c4999c77249432c03b32fa34c718819