Paper Citation Record · LEDGER

PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2205.11584.

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

pith.paper-citation-record.v1
2205.11584 v2

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 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:12:39.340407Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T23:07:14.175950Z

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 a4d276cc-1c9e-4aff-bd6c-f21c009189b5 · inbound

RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility cites this paper.

RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:07:14.178783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6d3a1b90-5d01-4cd3-b2d0-83193c3b7c65 · inbound

Mitigating Group-Level Fairness Disparities in Federated Visual Language Models cites this paper.

Mitigating Group-Level Fairness Disparities in Federated Visual Language Models PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T04:12:39.340407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79e31d50-fb8e-4696-ad18-76ac7dc087ca · inbound

Fairness in Federated Learning: Fairness for Whom? cites this paper.

Fairness in Federated Learning: Fairness for Whom? PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:19.025872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2f74810-d5ae-4104-b27b-006906409617 · inbound

The Fair Game: Auditing & Debiasing AI Algorithms Over Time cites this paper.

The Fair Game: Auditing & Debiasing AI Algorithms Over Time PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-05T22:45:20.733534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43b1158b-bc5b-4a96-a0fd-5ad40a2e65ed · inbound

Co-designing for Compliance: Multi-party Computation Protocols for Post-Market Fairness Monitoring in Algorithmic Hiring cites this paper.

Co-designing for Compliance: Multi-party Computation Protocols for Post-Market Fairness Monitoring in Algorithmic Hiring PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-09T02:29:20.718409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation af4281c1-c8ec-40d6-840e-92cac79f5bf8 · inbound

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning cites this paper.

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T14:37:19.053492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.