Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2212.01977.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:33:08.315250Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-04T21:06:27.634408Z
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 0f972ac8-43ec-42de-80a7-0cbee178e8ed · inbound
PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning cites this paper.
PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:33:08.315250Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8c527c3-bb92-48b9-b26c-0e183cc9e50d · inbound
Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.
Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Reference 96
- Resolution
- verified exact
- local_arxiv, observed 2026-08-04T21:06:27.638110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.