Paper Citation Record · LEDGER

Federated Learning of a Mixture of Global and Local Models

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2002.05516.

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

pith.paper-citation-record.v1
2002.05516 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:20:47.199277Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:37:27.356543Z

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 418ca33c-5eb2-4396-b4a2-1665f64a517b · inbound

Optimization Methods and Software for Federated Learning cites this paper.

Optimization Methods and Software for Federated Learning Federated Learning of a Mixture of Global and Local Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:47.199277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae8b9a34-5655-4794-b900-38f4d559d6a6 · 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 Federated Learning of a Mixture of Global and Local Models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-04T21:06:26.161787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfdcedfd-8b37-423d-8609-24a935726b63 · inbound

Collaborative and Efficient Fine-tuning: Leveraging Task Similarity cites this paper.

Collaborative and Efficient Fine-tuning: Leveraging Task Similarity Federated Learning of a Mixture of Global and Local Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T03:49:39.919263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bf4ed05-50bb-41cd-8b24-cb7ec7177f1e · inbound

Demystifying the Optimal Fair Classifier in Multi-Class Classification cites this paper.

Demystifying the Optimal Fair Classifier in Multi-Class Classification Federated Learning of a Mixture of Global and Local Models

Reference 112

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:52:35.616528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5d1a8cfe-51dd-4b54-a49a-5b8fd5449905 · inbound

Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning cites this paper.

Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning Federated Learning of a Mixture of Global and Local Models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:37:27.358523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3459d547-3d9a-41dd-918c-5d26ac6190f4 · inbound

Robust Decentralized Optimization under Node Failures via Adaptive Regularization cites this paper.

Robust Decentralized Optimization under Node Failures via Adaptive Regularization Federated Learning of a Mixture of Global and Local Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T14:27:27.998841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.