Paper Citation Record · LEDGER

Ditto: Fair and Robust Federated Learning Through Personalization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2012.04221.

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

pith.paper-citation-record.v1
2012.04221 v3

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 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:24:54.856941Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T16:44:56.415290Z

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 1dccb3b2-7941-41cc-a60f-caad5f710496 · inbound

Reliable Vertical Federated Learning in 5G Core Network Architecture cites this paper.

Reliable Vertical Federated Learning in 5G Core Network Architecture Ditto: Fair and Robust Federated Learning Through Personalization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:24:54.856941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bd02adb-95db-4ee0-ba2b-c8adcda505e7 · inbound

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization cites this paper.

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization Ditto: Fair and Robust Federated Learning Through Personalization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T10:44:00.164098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bfb37b8-85e0-4f05-a49d-4f45fa6747e6 · inbound

OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization cites this paper.

OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization Ditto: Fair and Robust Federated Learning Through Personalization

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:44:04.655591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 158c0295-d4cf-404c-8b9d-320f5afb7d9e · 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 Ditto: Fair and Robust Federated Learning Through Personalization

Reference 140

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T16:44:56.416639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.