Paper Citation Record · LEDGER

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks

As of 12 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2501.11848.

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

pith.paper-citation-record.v1
2501.11848 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:51:28.728550Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

45 of 45 outbound references displayed

verified exact0
verified fuzzy38
unresolved7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7c3a330e-409c-453e-9a0f-e821bd1d9e3b · outbound

This paper cites Secure and efficient federated learning with provable performance guarantees via stochastic quantization,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Secure and efficient federated learning with provable performance guarantees via stochastic quantization,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.542299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 42a1ffdd-4b3d-4f9f-a871-3fba46325194 · outbound

This paper cites Towards secure and verifiable hybrid federated learning,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Towards secure and verifiable hybrid federated learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.521489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dcbbca1c-ed86-4cf2-ae62-2e86fb1dc9a2 · outbound

This paper cites Reliable and in- terpretable personalized federated learning,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Reliable and in- terpretable personalized federated learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.502801Z

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Observation eff83b40-dcc4-4090-b4bd-84b97e12e74e · outbound

This paper cites Revisiting weighted aggregation in federated learning with neural networks,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Revisiting weighted aggregation in federated learning with neural networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.484343Z

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Observation 45f6af30-9b95-414a-96cb-934d3bdc3fad · outbound

This paper cites Feder- ated conformal predictors for distributed uncertainty quantification,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Feder- ated conformal predictors for distributed uncertainty quantification,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.465457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4c7ed5f8-2d85-444d-a30c-beb685b3a264 · outbound

This paper cites Multimodal federated learning via contrastive representation ensemble,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Multimodal federated learning via contrastive representation ensemble,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.446366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 31559b7b-7916-4cbf-8956-6276220bf629 · outbound

This paper cites The eu general data protection regu- lation (gdpr),.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks The eu general data protection regu- lation (gdpr),

Reference 7

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Source-reported events for the cited work

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Observation 35e583d5-4398-4b98-aade-e328256eec3e · outbound

This paper cites Understanding the scope and impact of the california consumer privacy act of 2018,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Understanding the scope and impact of the california consumer privacy act of 2018,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.412375Z

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Observation 02b4d96d-4c63-4573-9a9d-1e51f51e04ec · outbound

This paper cites Verifi: Towards verifiable federated unlearning,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Verifi: Towards verifiable federated unlearning,

Reference 9

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Source-reported events for the cited work

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Observation 6b985249-a411-4737-aeda-d806b6061045 · outbound

This paper cites Federaser: Enabling efficient client-level data removal from federated learning models,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Federaser: Enabling efficient client-level data removal from federated learning models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.380248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ed1cab67-6595-424c-ab5d-5a79fb1c3a40 · outbound

This paper cites Asynchronous federated unlearning,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Asynchronous federated unlearning,

Reference 11

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Source-reported events for the cited work

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Observation 8549fc27-1599-4a59-93a9-b9eea65183c8 · outbound

This paper cites Fast federated machine unlearning with nonlinear functional theory,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Fast federated machine unlearning with nonlinear functional theory,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.348398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c015681d-c523-42f4-b5bb-b034b171da64 · outbound

This paper cites Fedrecovery: Differentially private machine unlearning for federated learning frame- works,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Fedrecovery: Differentially private machine unlearning for federated learning frame- works,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.332962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6c4677ad-ac7a-4645-8a42-db6a6b0e0a43 · outbound

This paper cites Understanding black-box predictions via influence functions,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Understanding black-box predictions via influence functions,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.316955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 84173715-e060-4fc9-94e3-a649cfabfa7f · outbound

This paper cites VeriFi: Towards Verifiable Federated Unlearning.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks VeriFi: Towards Verifiable Federated Unlearning

Reference 15

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Source-reported events for the cited work

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Observation 45fd55e1-3aaf-45c0-b542-237365f28f5f · outbound

This paper cites Federated Unlearning with Knowledge Distillation.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Federated Unlearning with Knowledge Distillation

Reference 16

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Source-reported events for the cited work

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Observation 0d50959b-0e43-4b4b-bfbb-90fa43b61eb9 · outbound

This paper cites Federated unlearning via class- discriminative pruning,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Federated unlearning via class- discriminative pruning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.300452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3c678194-aae7-48d6-8a26-1a190ba0f08d · outbound

This paper cites The right to be forgotten in federated learning: An efficient realization with rapid retraining,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks The right to be forgotten in federated learning: An efficient realization with rapid retraining,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.284482Z

Source-reported events for the cited work

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Observation a57cc11a-8c51-47c3-b313-241062042862 · outbound

This paper cites Hidden poison: Machine unlearning enables camouflaged poisoning attacks,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Hidden poison: Machine unlearning enables camouflaged poisoning attacks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.267047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c6eb1dd0-434c-4da7-b757-b3bd6cda9e9d · outbound

This paper cites Towards understanding and enhancing robustness of deep learning models against malicious unlearning attacks,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Towards understanding and enhancing robustness of deep learning models against malicious unlearning attacks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.249794Z

Source-reported events for the cited work

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Observation df5cded7-810a-4e8c-a749-bf0056e0bc3a · outbound

This paper cites Static and sequential malicious attacks in the context of selective forgetting,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Static and sequential malicious attacks in the context of selective forgetting,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.232988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2dbc439f-05a5-4e64-9cd4-a40138e3254f · outbound

This paper cites A duty to forget, a right to be assured? exposing vulnerabilities in machine unlearning services,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks A duty to forget, a right to be assured? exposing vulnerabilities in machine unlearning services,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.216585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 98693db6-d4d4-4f9f-b475-90ffbec3d9a1 · outbound

This paper cites Resolving training biases via influence-based data relabeling,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Resolving training biases via influence-based data relabeling,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 875151f9-b209-4e10-9040-86c0a7899fcb · outbound

This paper cites Regularizing second-order influences for continual learning,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Regularizing second-order influences for continual learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.184373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 03b48881-1d5d-499c-80e8-b15711dc1f81 · outbound

This paper cites Understanding influence functions and data models via harmonic analysis,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Understanding influence functions and data models via harmonic analysis,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.168618Z

Source-reported events for the cited work

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Observation f129cd37-02e4-4e9a-a012-97690b74e50a · outbound

This paper cites Representer point selection for explaining regularized high- dimensional models,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Representer point selection for explaining regularized high- dimensional models,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.152188Z

Source-reported events for the cited work

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Observation 1a2cc957-2b1a-493b-a2cb-931bd2a57941 · outbound

This paper cites Hydra: Hypergradient data relevance analysis for interpreting deep neural networks,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Hydra: Hypergradient data relevance analysis for interpreting deep neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.135428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 03743097-972f-49e3-b14f-549b9e6e0857 · outbound

This paper cites Fastif: Scalable influence functions for efficient model interpretation and debugging,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Fastif: Scalable influence functions for efficient model interpretation and debugging,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.119266Z

Source-reported events for the cited work

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Observation 8e3ceded-da6e-444a-9cca-9084f34ee1ea · outbound

This paper cites Scaling up influence functions,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Scaling up influence functions,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.103216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b543a86d-029d-494c-9d96-18a443cf61eb · outbound

This paper cites Flpu- rifier: Backdoor defense in federated learning via decoupled contrastive training,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Flpu- rifier: Backdoor defense in federated learning via decoupled contrastive training,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.087264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 15f63d70-9bfe-4327-a053-49a4d9db0bbb · outbound

This paper cites Reverse backdoor distillation: Towards online backdoor attack detection for deep neural network models,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Reverse backdoor distillation: Towards online backdoor attack detection for deep neural network models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.071859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d68b33a8-f1c7-4e6e-b835-8443358ad99e · outbound

This paper cites Can we mitigate backdoor attack using adversarial detection methods?.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Can we mitigate backdoor attack using adversarial detection methods?

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.055297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f37aec9f-814f-45d4-bf98-9a5d2f7fa49d · outbound

This paper cites Anti-backdoor learning: Training clean models on poisoned data,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Anti-backdoor learning: Training clean models on poisoned data,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.038222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1b1def8b-4d2e-4b29-8bb4-355f8b77fa0c · outbound

This paper cites De-pois: An attack- agnostic defense against data poisoning attacks,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks De-pois: An attack- agnostic defense against data poisoning attacks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:29.020717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a9257c17-44d4-4e7a-8ff0-fd0a3097f31f · outbound

This paper cites an unresolved cited work.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:51:29.003506Z

Source-reported events for the cited work

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Observation b0a69c57-41f1-4b8a-b906-3de27735db53 · outbound

This paper cites Membership inference attacks against machine learning models,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Membership inference attacks against machine learning models,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.985822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c81c5871-5573-4ffc-a365-6bb8d521be98 · outbound

This paper cites Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.966533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eb2d3030-9820-4c52-a359-3d4e3435b63d · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Communication-efficient learning of deep networks from decentralized data,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.948954Z

Source-reported events for the cited work

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Observation 70123c4f-dc34-4b10-835e-3839a3b2387d · outbound

This paper cites Byzantine-robust dis- tributed learning: Towards optimal statistical rates,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Byzantine-robust dis- tributed learning: Towards optimal statistical rates,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.931416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6f8d44ad-d8ad-4d1c-b75c-1ba4e2700db8 · outbound

This paper cites Machine learning with adversaries: Byzantine tolerant gradient descent,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Machine learning with adversaries: Byzantine tolerant gradient descent,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.910677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8430ca6a-f6cb-496f-b07a-1915336802fd · outbound

This paper cites Fat: Federated adversarial training,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Fat: Federated adversarial training,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.892155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 496f81ef-2bee-4bac-bd12-ddeb59a6bbca · outbound

This paper cites Fadngs: Federated learning for anomaly detection,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Fadngs: Federated learning for anomaly detection,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.872023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 91145747-f93d-4574-9109-671b5c2fc3f0 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Very deep convolutional networks for large-scale image recognition,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.852565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8a9f5da6-ef54-442b-bc05-99d4f8ec0518 · outbound

This paper cites Deep residual learning for image recognition,.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Deep residual learning for image recognition,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:51:28.834901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f2bbd228-29f5-493c-a08d-50ab2631ec0e · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T17:51:28.728550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.