Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning | Paper Citation Record

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This paper cites an unresolved cited work.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Unresolved cited work

Reference 1

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Observation 96ba4f03-96c2-4535-b186-87f63874cd2e · outbound

This paper cites Sparse Communication for Distributed Gradient Descent.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Sparse Communication for Distributed Gradient Descent

Reference 2

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Observation 19a16598-578f-4af3-9bd0-5ff5cefd0612 · outbound

This paper cites How to backdoor federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning How to backdoor federated learning

Reference 3

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Observation b319cac7-4bb0-4d78-acf1-6c0fcb26e26f · outbound

This paper cites A little is enough: Circumventing defenses for distributed learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning A little is enough: Circumventing defenses for distributed learning

Reference 4

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Observation 7c4cbb3d-1fec-45e2-a085-00155eab1909 · outbound

This paper cites Analyzing federated learning through an adversarial lens.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Analyzing federated learning through an adversarial lens

Reference 5

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Observation 64ea39b6-a431-441f-98bd-ec84f7d5d36d · outbound

This paper cites Poisoning attacks against support vector machines.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Poisoning attacks against support vector machines

Reference 6

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Observation 1c84e5d6-8156-425e-bddd-8022fe2d64f3 · outbound

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

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Machine learning with adversaries: Byzantine tolerant gradient descent

Reference 7

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Observation 087293d8-50f8-4615-852e-f3cf8742270b · outbound

This paper cites Fltrust: Byzantine-robust federated learning via trust bootstrapping.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Fltrust: Byzantine-robust federated learning via trust bootstrapping

Reference 8

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Observation cfe68e1d-c210-4156-b4b0-cefa9f679323 · outbound

This paper cites Membership inference attacks from first principles.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Membership inference attacks from first principles

Reference 9

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Observation deca3a9f-6a53-4bc3-bf61-deddd60f842e · outbound

This paper cites Gan- leaks: A taxonomy of membership inference attacks against generative models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Gan- leaks: A taxonomy of membership inference attacks against generative models

Reference 10

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Observation 8b7e60c2-0712-462b-9388-cd4fc921b09d · outbound

This paper cites Label-only membership in- ference attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Label-only membership in- ference attacks

Reference 11

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Observation e761cd8d-7d44-4916-b8d6-1bbd007babdf · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning An analysis of single-layer networks in unsupervised feature learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:52.462338Z

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Observation 88c1a28b-2de6-4482-8dd2-30d26bd8cc8f · outbound

This paper cites Differential privacy: A survey of results.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Differential privacy: A survey of results

Reference 13

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Observation b0082503-c812-4a49-a74b-8ad3c94922f9 · outbound

This paper cites Local model poisoning attacks to byzantine-robust federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Local model poisoning attacks to byzantine-robust federated learning

Reference 14

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This paper cites Aflguard: Byzantine-robust asynchronous federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Aflguard: Byzantine-robust asynchronous federated learning

Reference 15

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Observation f9f11340-3d07-43d0-a0da-52fce78c41c5 · outbound

This paper cites Byzantine- robust decentralized federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Byzantine- robust decentralized federated learning

Reference 16

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Observation 7c95d37f-4606-4338-9e00-eb980b2f2714 · outbound

This paper cites Byzantine-robust federated learning over ring-all-reduce dis- tributed computing.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Byzantine-robust federated learning over ring-all-reduce dis- tributed computing

Reference 17

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Source-reported events for the cited work

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Observation d769cc9c-9649-4d2b-854a-191aac23f172 · outbound

This paper cites Do we really need to design new byzantine-robust aggregation rules? In NDSS, 2025.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Do we really need to design new byzantine-robust aggregation rules? In NDSS, 2025

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:50.414227Z

Source-reported events for the cited work

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Observation 4d700dd1-b15c-421e-b0c5-6fa353d74635 · outbound

This paper cites Provably robust federated reinforcement learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Provably robust federated reinforcement learning

Reference 19

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Observation 2802a161-ae71-4818-8a88-11dc123c372d · outbound

This paper cites Active inference against federated learning: Attacks and solutions.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Active inference against federated learning: Attacks and solutions

Reference 20

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This paper cites Chal- lenges in representation learning: A report on three machine learning contests.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Chal- lenges in representation learning: A report on three machine learning contests

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:49.615070Z

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Observation f879ed53-9346-432d-acf6-ab289d7f0de9 · outbound

This paper cites Membership inference at- tacks on machine learning: A survey.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Membership inference at- tacks on machine learning: A survey

Reference 22

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Observation 1ab2bb9a-0b7d-458d-8245-623b65464386 · outbound

This paper cites Advances and open problems in federated learn- ing.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Advances and open problems in federated learn- ing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:49.113185Z

Source-reported events for the cited work

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This paper cites Maximizing the spread of influence through a social network.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Maximizing the spread of influence through a social network

Reference 24

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This paper cites Adam: A Method for Stochastic Optimization.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Adam: A Method for Stochastic Optimization

Reference 25

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Observation 382ffa56-80e7-42f8-9dbe-82f6e60b5125 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Federated Learning: Strategies for Improving Communication Efficiency

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:23:37.105303Z

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Observation f4c6c4bf-114a-4969-b480-7ee0929e1f39 · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T21:23:37.230172Z

Source-reported events for the cited work

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Observation d9977661-4565-40a8-99be-c5a050d954cb · outbound

This paper cites Learning multiple layers of features from tiny images.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Learning multiple layers of features from tiny images

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:48.445630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 653baaac-5997-4290-9256-a7b3c1a63b2d · outbound

This paper cites Stolen memories: Leverag- ing model memorization for calibrated white-box member- ship inference.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Stolen memories: Leverag- ing model memorization for calibrated white-box member- ship inference

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:48.111821Z

Source-reported events for the cited work

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Observation 2a7c0316-7bf5-4da9-8528-19e58a5c8bb5 · outbound

This paper cites Learning to at- tack federated learning: A model-based reinforcement learn- ing attack framework.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Learning to at- tack federated learning: A model-based reinforcement learn- ing attack framework

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:47.880657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b0aeb968-db5f-4f8a-ac57-84948f658634 · outbound

This paper cites Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare

Reference 31

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Observation 7bcc8632-c21b-47a2-af6b-2e06958e5476 · outbound

This paper cites Loden: Making every client in federated learning a defender against the poisoning membership infer- ence attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Loden: Making every client in federated learning a defender against the poisoning membership infer- ence attacks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:47.633489Z

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Observation beffa024-e20a-4dc3-88b8-391cd06553fd · outbound

This paper cites Prop- erty inference from poisoning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Prop- erty inference from poisoning

Reference 33

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This paper cites Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag ¨uera y Arcas.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag ¨uera y Arcas

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:47.068589Z

Source-reported events for the cited work

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Observation adcadde4-6556-424c-a37b-f4a90aba74fd · outbound

This paper cites Towards poisoning of deep learning algorithms with back-gradient optimization.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Towards poisoning of deep learning algorithms with back-gradient optimization

Reference 35

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Observation adce41fd-9a1f-42e1-aa49-4f4d911f0376 · outbound

This paper cites Compre- hensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Compre- hensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:46.415972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5d4502bd-7e35-4ad4-916d-0fabc8bb9aef · outbound

This paper cites On the difficulty of member- ship inference attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning On the difficulty of member- ship inference attacks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:46.071049Z

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This paper cites Deepsight: Mitigating backdoor at- tacks in federated learning through deep model inspection.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Deepsight: Mitigating backdoor at- tacks in federated learning through deep model inspection

Reference 38

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This paper cites Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:45.437896Z

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Observation 96b062d9-26f4-4276-b636-eeccea2c3e79 · outbound

This paper cites Membership inference attacks against machine learning models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Membership inference attacks against machine learning models

Reference 40

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Observation d876fd69-03ba-4028-a9e2-1b96360dbd39 · outbound

This paper cites Membership inference attacks against adversarially robust deep learning models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Membership inference attacks against adversarially robust deep learning models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:44.831240Z

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Observation 7c0c72cb-e70a-42c2-b5b8-dfa4ba0876f9 · outbound

This paper cites Privacy risks of securing machine learning models against adversarial ex- amples.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Privacy risks of securing machine learning models against adversarial ex- amples

Reference 42

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Observation d6d228fa-fd37-4cde-b04d-f25e670916c2 · outbound

This paper cites Can You Really Backdoor Federated Learning?.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Can You Really Backdoor Federated Learning?

Reference 43

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Source-reported events for the cited work

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Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Data poisoning attacks against federated learning systems

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:44.350521Z

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Observation cffc2079-44e5-47ad-97ed-a3e271677ca4 · outbound

This paper cites Towards Demystifying Membership Inference Attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Towards Demystifying Membership Inference Attacks

Reference 45

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This paper cites Poisoning attacks and defenses to federated unlearning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Poisoning attacks and defenses to federated unlearning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:44.108715Z

Source-reported events for the cited work

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Observation a28e3ae1-e3bf-4518-9e47-725b547b0177 · outbound

This paper cites Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 47

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Source-reported events for the cited work

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Observation fd4dffde-bc5e-473e-b3b4-03728c9160dc · outbound

This paper cites Generalized Byzantine-tolerant SGD.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Generalized Byzantine-tolerant SGD

Reference 48

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Source-reported events for the cited work

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Observation 35635d96-f408-4c54-aa95-772f5d245a55 · outbound

This paper cites Phocas: dimensional Byzantine-resilient stochastic gradient descent.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Phocas: dimensional Byzantine-resilient stochastic gradient descent

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T21:23:39.992673Z

Source-reported events for the cited work

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Observation 3d38569a-6c0a-4cc9-b41c-f7d0e6b99f53 · outbound

This paper cites Fe- dredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Fe- dredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:43.797975Z

Source-reported events for the cited work

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Observation 01bc5592-c106-4422-9cd8-fd270fdb3208 · outbound

This paper cites Robust federated learning mitigates client-side train- ing data distribution inference attacks.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Robust federated learning mitigates client-side train- ing data distribution inference attacks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:43.525781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 05a32bb4-23da-4fbb-a321-af38ef244d51 · outbound

This paper cites Federated machine learning: Concept and applications.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Federated machine learning: Concept and applications

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:43.216575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 86e6bcfd-61a0-42a8-9440-68afcad57c4e · outbound

This paper cites Enhanced membership in- ference attacks against machine learning models.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Enhanced membership in- ference attacks against machine learning models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:42.967113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d197e004-de7a-4757-8e44-75f23fbda67b · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:42.701810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 46139d39-32b7-41b0-8ec3-2d34cc18ba44 · outbound

This paper cites Byzantine-robust distributed learning: Towards op- timal statistical rates.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Byzantine-robust distributed learning: Towards op- timal statistical rates

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:42.413805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8f89a9b6-b60e-45ae-8984-52b88a5ea233 · outbound

This paper cites Poisoning federated recommender systems with fake users.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Poisoning federated recommender systems with fake users

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:42.137262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 69fa18d1-d3d7-4b7d-8336-de4dc57c8739 · outbound

This paper cites Agrevader: Poisoning membership inference against byzantine-robust federated learning.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Agrevader: Poisoning membership inference against byzantine-robust federated learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:41.877820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c85cf7ea-a83f-42c6-80b3-a0c90d37d49f · outbound

This paper cites Pn−b i=b+1(θi − ω) n − 2b #2 ≤ max   .

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Pn−b i=b+1(θi − ω) n − 2b #2 ≤ max   

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:23:41.626723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers