Paper Citation Record · LEDGER

Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2110.13057.

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

pith.paper-citation-record.v1
2110.13057 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 2026-08-12T11:26:16.939288Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:39:35.019824Z

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 b8116c95-82d9-4f84-80ae-c3a044e26366 · inbound

Hidden Data Privacy Breaches in Federated Learning cites this paper.

Hidden Data Privacy Breaches in Federated Learning Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T11:26:16.939288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b34d7c93-3f9d-4df6-9fb0-02807f1d3c3e · inbound

A Survey of Secure Semantic Communications cites this paper.

A Survey of Secure Semantic Communications Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T22:44:11.391693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a77a8abc-865a-45fb-a430-7db2f5062bb2 · inbound

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling cites this paper.

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:05:45.506545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd5b3fd7-6f09-4a33-b7a5-de293c771d5f · inbound

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks cites this paper.

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T20:30:46.357741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 569fbc1a-4eb9-4374-9a70-e2bbcb433954 · inbound

From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning cites this paper.

From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:39:35.021336Z

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 50427c0e-df0f-4ea4-90a3-fcef341eda1e · inbound

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks cites this paper.

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-02T02:54:03.989583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.