Paper Citation Record · LEDGER

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning

As of 21 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.11097.

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

pith.paper-citation-record.v1
2505.11097 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:04:05.475979Z

measured 40 of 40 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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verified fuzzy18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e04f01b2-df9b-450a-b7b0-213d07c77fa0 · outbound

This paper cites The right to be forgotten.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning The right to be forgotten

Reference 1

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Observation 0b83492a-1b69-4c92-837b-c87302a43ada · outbound

This paper cites The california consumer privacy act: Towards a european-style privacy regime in the united states.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning The california consumer privacy act: Towards a european-style privacy regime in the united states

Reference 2

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Observation c54c3e39-ab32-4542-b1d7-8cf695b588b2 · outbound

This paper cites Federated Unlearning.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Federated Unlearning

Reference 3

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Observation ce610347-3cc0-43da-8de0-25924a3ce147 · outbound

This paper cites Federated unlearning in financial applications.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Federated unlearning in financial applications

Reference 4

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Observation 31e2fca0-c604-4ae2-a73a-debcf453b0a8 · outbound

This paper cites Verifi: Towards verifiable federated unlearning.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Verifi: Towards verifiable federated unlearning

Reference 5

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Observation e5d2af98-2584-41c5-bc2c-0e9cdf037b30 · outbound

This paper cites Proof of unlearning: Definitions and instantiation.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Proof of unlearning: Definitions and instantiation

Reference 6

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Observation ab5f6de2-5f5e-411a-9bb2-828191107a4f · outbound

This paper cites Feder- ated learning with blockchain-enhanced machine unlearning: A trustworthy approach.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Feder- ated learning with blockchain-enhanced machine unlearning: A trustworthy approach

Reference 7

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Observation 89cf37b6-4f8b-4e6c-a84f-6c02ef1bc55f · outbound

This paper cites In29th USENIX security symposium (USENIX Security 20), pages 1291–1308, 2020.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning In29th USENIX security symposium (USENIX Security 20), pages 1291–1308, 2020

Reference 8

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Observation abf5d009-fba6-49da-a9ca-cb76d6cc6922 · outbound

This paper cites Federated Unlearning with Knowledge Distillation.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Federated Unlearning with Knowledge Distillation

Reference 9

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Observation 3f8b5ed5-2adc-4e68-8bbb-96569cc9c5e5 · outbound

This paper cites Federated unlearning via class-discriminative pruning.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Federated unlearning via class-discriminative pruning

Reference 10

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Observation 0efb5df2-bc48-48ec-89fa-4554554716b5 · outbound

This paper cites Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter

Reference 11

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Observation a92d6e86-af43-47f4-afed-87b82a346dca · outbound

This paper cites When the curious abandon honesty: Federated learning is not private.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning When the curious abandon honesty: Federated learning is not private

Reference 12

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Observation a1e34037-afd6-4f9b-8f8e-da5da6ddbb3d · outbound

This paper cites Privacy-preserving federated learning with malicious clients and honest-but-curious servers.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Privacy-preserving federated learning with malicious clients and honest-but-curious servers

Reference 13

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Observation 43c68b53-a428-47fb-a9e1-5f5c9941511d · outbound

This paper cites Deep leakage from gradients.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Deep leakage from gradients

Reference 14

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Observation 00464b6f-03cf-407b-b565-844058cf7c4b · outbound

This paper cites Inverting gradients- how easy is it to break privacy in federated learning?Advances in neural information processing systems, 33:16937–16947, 2020.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Inverting gradients- how easy is it to break privacy in federated learning?Advances in neural information processing systems, 33:16937–16947, 2020

Reference 15

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Observation b8634583-0326-40dc-b593-c96a33ce06cb · outbound

This paper cites Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning

Reference 16

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Observation cb13e445-8116-4adb-858a-16535b37e6c8 · outbound

This paper cites On the necessity of auditable algorithmic definitions for machine unlearning.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning On the necessity of auditable algorithmic definitions for machine unlearning

Reference 17

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Observation eb5785b1-bf19-463d-926d-3c2868714979 · outbound

This paper cites Auditing privacy defenses in federated learning via generative gradient leakage.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Auditing privacy defenses in federated learning via generative gradient leakage

Reference 18

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This paper cites The right to be forgotten in feder- ated learning: An efficient realization with rapid retraining.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning The right to be forgotten in feder- ated learning: An efficient realization with rapid retraining

Reference 19

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Observation 3351a970-dd75-4732-920a-d8d1e3266a66 · outbound

This paper cites Federated Unlearning: How to Efficiently Erase a Client in FL?.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 20

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Observation 3ddb78a0-e4f8-49c3-8df0-fca5c9c39ef4 · outbound

This paper cites Fedu: Federated unlearning via user-side influence approximation forgetting.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Fedu: Federated unlearning via user-side influence approximation forgetting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:06.027646Z

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Observation 95ecc487-aae0-4210-856d-cd5b3a44e1c6 · outbound

This paper cites Update selective parameters: Federated machine unlearning based on model explanation.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Update selective parameters: Federated machine unlearning based on model explanation

Reference 22

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Observation f12c3056-99d2-45af-bd11-52c687ec088b · outbound

This paper cites Unlearning during Learning: An Efficient Federated Machine Unlearning Method.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Unlearning during Learning: An Efficient Federated Machine Unlearning Method

Reference 23

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Observation 28cf0d0b-2d6b-4deb-93fe-47bcbe785752 · outbound

This paper cites Fedmua: Exploring the vulnerabilities of federated learning to malicious unlearning attacks.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Fedmua: Exploring the vulnerabilities of federated learning to malicious unlearning attacks

Reference 24

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Observation e232818d-be1b-4fb8-8f40-59189e6b8b24 · outbound

This paper cites Poisoning Attacks and Defenses to Federated Unlearning.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Poisoning Attacks and Defenses to Federated Unlearning

Reference 25

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This paper cites Generative gradient inversion via over-parameterized networks in federated learning.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Generative gradient inversion via over-parameterized networks in federated learning

Reference 26

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Gradient inversion with generative image prior

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This paper cites Gifd: A generative gradi- ent inversion method with feature domain optimization.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Gifd: A generative gradi- ent inversion method with feature domain optimization

Reference 28

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Unresolved cited work

Reference 29

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This paper cites Learning to invert: Simple adaptive attacks for gradient inversion in federated learning.

Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Learning to invert: Simple adaptive attacks for gradient inversion in federated learning

Reference 30

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Communication-efficient learning of deep networks from decentralized data

Reference 31

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning In Encyclopedia of Mathematics

Reference 32

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Learning multiple layers of features from tiny images

Reference 33

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Gradient-based learning applied to document recognition

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning The unrea- sonable effectiveness of deep features as a perceptual metric

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Deep residual learning for image recognition

Reference 37

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Federated optimization in heterogeneous networks

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Adaptive Federated Optimization

Reference 39

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Verifiably Forgotten? Gradient Differences Still Enable Data Reconstruction in Federated Unlearning Feature hashing for large scale multitask learning

Reference 40

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