Paper Citation Record · LEDGER

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2102.09743.

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

pith.paper-citation-record.v1
2102.09743 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:44:26.192862Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T16:44:56.453180Z

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 e2203f42-215c-4047-b8a3-287ccc3bef08 · inbound

PFedDST: Personalized Federated Learning with Decentralized Selection Training cites this paper.

PFedDST: Personalized Federated Learning with Decentralized Selection Training Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T11:44:26.192862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa1d872a-ab4f-433f-808d-b24b451e4239 · inbound

FedAPM: Federated Learning via ADMM with Partial Model Personalization cites this paper.

FedAPM: Federated Learning via ADMM with Partial Model Personalization Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:54.276565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 596f72c0-f3d2-44d1-8772-afd4358f6193 · 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 Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-04T21:06:26.168118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9770d644-13ed-495f-be59-72f33a56e875 · inbound

Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage cites this paper.

Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:44:56.454647Z

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.