Paper Citation Record · LEDGER

Federated Unlearning: How to Efficiently Erase a Client in FL?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2207.05521.

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

pith.paper-citation-record.v1
2207.05521 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:39:53.221771Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:13:52.704858Z

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 065522c8-6871-4441-b671-cbf0eb864548 · inbound

FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher cites this paper.

FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:53:29.896049Z

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 c31cac37-95fc-4237-ac23-9998ffd8eb59 · inbound

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning cites this paper.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T13:39:53.221771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5ff672a-f443-4911-a8bd-748db3a2eb37 · inbound

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention cites this paper.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T23:04:31.301605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d1bff47-ae2c-436d-b718-ffcd09ba8426 · inbound

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems cites this paper.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:11.076343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b95df730-063d-42a2-8dba-f91d45555abf · inbound

SecureT2I: No More Unauthorized Manipulation on AI Generated Images from Prompts cites this paper.

SecureT2I: No More Unauthorized Manipulation on AI Generated Images from Prompts Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T20:12:35.895592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d831b6e-0e89-449c-8343-5606c50f9289 · inbound

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning cites this paper.

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T17:58:43.561249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4abfc41-71af-459f-8ba2-8044e84eeb0e · inbound

On the importance of multiple training seeds for evaluating machine unlearning cites this paper.

On the importance of multiple training seeds for evaluating machine unlearning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T07:09:15.117844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e72025e-ee50-4905-bb56-cc9305316ab1 · inbound

Lethe: Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning cites this paper.

Lethe: Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T06:35:47.269530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9d71f23-be55-4917-a3ad-a2cc5838fc5c · inbound

Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement cites this paper.

Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:28:02.269924Z

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 85d31efc-cebb-4bae-83fd-5471159e97ef · inbound

Representation-Guided Parameter-Efficient LLM Unlearning cites this paper.

Representation-Guided Parameter-Efficient LLM Unlearning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:06:19.169548Z

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 fcd6bb6e-cdc9-4d37-b3aa-7b9a8831d4d0 · inbound

Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging cites this paper.

Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:31:25.266853Z

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 bdd1a7ca-8e4c-4158-918e-326ae8768caf · inbound

EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure cites this paper.

EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:11:12.222034Z

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 dff6719a-12b3-47d2-9d74-84a62a602f45 · inbound

Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition cites this paper.

Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:26:06.791343Z

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 135ee4c7-046f-4109-b3dc-458a921c1e48 · inbound

Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions cites this paper.

Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:29:53.122634Z

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 d2671580-7725-4e96-8d0c-1c61e626fedd · inbound

SCALE: Sensitivity-Aware Federated Unlearning with Information Freshness Optimization for Mobile Edge Computing cites this paper.

SCALE: Sensitivity-Aware Federated Unlearning with Information Freshness Optimization for Mobile Edge Computing Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-22T01:54:31.415387Z

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 b5a261c8-533a-49e2-9d27-ce127e39c796 · inbound

Image Feature Fusion-based Federated Client Unlearning (FCU) cites this paper.

Image Feature Fusion-based Federated Client Unlearning (FCU) Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:13:52.706847Z

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 9c5515ec-a7f1-40ed-9219-0b7545120775 · inbound

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning cites this paper.

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-15T10:46:07.438824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.