Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
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 3 inbound Pith citation observations for arXiv:2102.09743.
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 2026-08-07T10:43:54.276565Z
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 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.