Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy | Paper Citation Record

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This paper cites Advances and Open Problems in Federated Learning.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Advances and Open Problems in Federated Learning

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This paper cites Feddmc: Efficient and robust federated learning via detecting malicious clients,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Feddmc: Efficient and robust federated learning via detecting malicious clients,

Reference 2

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This paper cites Fedproc: Prototypical contrastive federated learning on non-iid data,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Fedproc: Prototypical contrastive federated learning on non-iid data,

Reference 3

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This paper cites Federated learning: Collaborative ma- chine learning without centralized training data,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Federated learning: Collaborative ma- chine learning without centralized training data,

Reference 4

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This paper cites Federated evaluation and tuning for on-device personalization: System design & and applications,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Federated evaluation and tuning for on-device personalization: System design & and applications,

Reference 5

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This paper cites Fate: An industrial grade platform for collaborative learning with data protection,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Fate: An industrial grade platform for collaborative learning with data protection,

Reference 6

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Observation 21bc9ea3-d2cd-4230-bbaf-d085e29004ec · outbound

This paper cites Deep leakage from gradients,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Deep leakage from gradients,

Reference 7

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Observation ced6a892-cfa4-434c-9c05-21a3342a0026 · outbound

This paper cites Using highly compressed gradients in federated learning for data reconstruction attacks,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Using highly compressed gradients in federated learning for data reconstruction attacks,

Reference 8

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Observation 8a120277-1be8-47b5-a97a-bebd8e1bd0b7 · outbound

This paper cites See through gradients: Image batch recovery via gradinversion,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy See through gradients: Image batch recovery via gradinversion,

Reference 9

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This paper cites Robbing the fed: Directly obtaining private data in federated learn- ing with modified models,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Robbing the fed: Directly obtaining private data in federated learn- ing with modified models,

Reference 10

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Observation 764b87eb-1db3-4e30-b467-20416664c422 · outbound

This paper cites When the curious abandon honesty: Federated learn- ing is not private,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy When the curious abandon honesty: Federated learn- ing is not private,

Reference 11

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Observation 5eec396c-8b00-44f7-8c8e-077fc11d8e69 · outbound

This paper cites A framework for evaluating gradient leakage attacks in federated learning,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy A framework for evaluating gradient leakage attacks in federated learning,

Reference 12

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Observation 477c1cb9-07d1-41ab-93ec-0b9a8dfc479e · outbound

This paper cites Eluding secure aggregation in federated learning via model inconsistency,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Eluding secure aggregation in federated learning via model inconsistency,

Reference 13

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This paper cites Dreaming to distill: Data-free knowledge transfer via deepinversion,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Dreaming to distill: Data-free knowledge transfer via deepinversion,

Reference 14

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Observation cff1622d-f5f8-427c-b7c9-1c37cc24a634 · outbound

This paper cites Reconstructing individual data points in federated learning hardened with differential privacy and secure aggregation,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Reconstructing individual data points in federated learning hardened with differential privacy and secure aggregation,

Reference 15

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Observation 6c8b37d2-9953-4a67-97f4-6dc4f8d52d30 · outbound

This paper cites Gradient inversion with generative image prior,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Gradient inversion with generative image prior,

Reference 16

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Observation 818a3362-5165-4a5c-9ae8-4429a4509c2f · outbound

This paper cites Gradvit: Gradient inversion of vision transformers,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Gradvit: Gradient inversion of vision transformers,

Reference 17

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Observation 73614c0b-784a-4025-a198-ae6041e6ad83 · outbound

This paper cites Analyzing user-level privacy attack against federated learning,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Analyzing user-level privacy attack against federated learning,

Reference 18

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Observation dd2dd6f8-9faa-4bbb-a7a0-0274e5778c46 · outbound

This paper cites Cafe: Catas- trophic data leakage in vertical federated learning,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Cafe: Catas- trophic data leakage in vertical federated learning,

Reference 19

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This paper cites R-gap: Recursive gradient attack on privacy,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy R-gap: Recursive gradient attack on privacy,

Reference 20

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Observation fd6c7018-d51f-4e5b-8634-d8eb9744ef98 · outbound

This paper cites Federated learning with differential privacy: Algorithms and performance analysis,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Federated learning with differential privacy: Algorithms and performance analysis,

Reference 21

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Observation 8473a74a-167c-45ab-93c0-f35efe0e9734 · outbound

This paper cites A differentially private federated learning model against poisoning attacks in edge computing,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy A differentially private federated learning model against poisoning attacks in edge computing,

Reference 22

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Observation c7d5dd16-c3ef-40d7-b153-50f8ff7159bc · outbound

This paper cites Personalized federated learning with differential privacy,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Personalized federated learning with differential privacy,

Reference 23

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Observation f6663f9d-643e-4e44-a8c1-147cf9cb39c2 · outbound

This paper cites Efficient differentially private secure aggregation for federated learning via hardness of learning with errors,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Efficient differentially private secure aggregation for federated learning via hardness of learning with errors,

Reference 24

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Observation fde50f1c-44ee-4471-8751-beb384d5ce8a · outbound

This paper cites Exploring the security boundary of data reconstruction via neuron exclusivity analysis,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Exploring the security boundary of data reconstruction via neuron exclusivity analysis,

Reference 25

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This paper cites Model inversion attack by integration of deep generative models: Privacy-sensitive face generation from a face recognition system,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Model inversion attack by integration of deep generative models: Privacy-sensitive face generation from a face recognition system,

Reference 26

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This paper cites On the inadequacy of similarity- based privacy metrics: Reconstruction attacks against.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy On the inadequacy of similarity- based privacy metrics: Reconstruction attacks against

Reference 27

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Observation 1d445d0b-535c-4e3c-815c-211a06507618 · outbound

This paper cites Beyond class-level privacy leakage: Breaking record-level privacy in federated learning,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Beyond class-level privacy leakage: Breaking record-level privacy in federated learning,

Reference 28

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Observation 4cfc251c-8d1c-4ca2-9d1d-2933029320dc · outbound

This paper cites Generative adversarial networks,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Generative adversarial networks,

Reference 29

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Observation a82c2b4f-201c-4b3e-a780-6394fe45b51b · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentral- ized Data,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Communication-Efficient Learning of Deep Networks from Decentral- ized Data,

Reference 30

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This paper cites The algorithmic foundations of differential privacy,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy The algorithmic foundations of differential privacy,

Reference 31

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Observation 4a091dde-0e51-4bea-b50f-b0343edf0ca1 · outbound

This paper cites Learning differ- entially private recurrent language models,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Learning differ- entially private recurrent language models,

Reference 32

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Observation c44c372b-7892-4bb5-af03-f2d5ec9beb26 · outbound

This paper cites Local and central differential privacy for robustness and privacy in federated learning,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Local and central differential privacy for robustness and privacy in federated learning,

Reference 33

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Observation 98fdc36c-fa42-4df5-8808-12b29633acca · outbound

This paper cites Eluding secure aggregation in federated learning via model inconsistency,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Eluding secure aggregation in federated learning via model inconsistency,

Reference 34

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Observation 51c0ab02-c70b-43e6-87a9-9df2d12a44c7 · outbound

This paper cites Inverting gradi- ents - how easy is it to break privacy in federated learning?,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Inverting gradi- ents - how easy is it to break privacy in federated learning?,

Reference 35

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Observation 40f98308-aa64-4885-9310-b44515635e07 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy ImageNet Large Scale Visual Recognition Challenge,

Reference 36

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Observation a550b3a6-c7a6-4c01-9be5-b261cdede390 · outbound

This paper cites Segment Anything.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Segment Anything

Reference 37

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Observation 1ce87ea9-bc25-4025-ac36-6cede8e8d2be · outbound

This paper cites A Comprehensive Survey on Segment Anything Model for Vision and Beyond.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy A Comprehensive Survey on Segment Anything Model for Vision and Beyond

Reference 38

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Observation da38be55-5fee-4303-8f69-e055cb097ccf · outbound

This paper cites Segment anything in medical images,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Segment anything in medical images,

Reference 39

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Observation b8c42cf8-942d-4ac6-9fbc-a1af1fcf76c5 · outbound

This paper cites Segment anything in non-euclidean domains: Challenges and opportunities,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Segment anything in non-euclidean domains: Challenges and opportunities,

Reference 40

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Observation 00c5e8cd-dbb8-4166-b4a0-d3de46427a5b · outbound

This paper cites A generalization of the half-normal distribution with applications to lifetime data,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy A generalization of the half-normal distribution with applications to lifetime data,

Reference 41

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Observation 6c655148-5fd4-4822-a2c9-fe2983434dd6 · outbound

This paper cites an unresolved cited work.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Unresolved cited work

Reference 42

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Observation 876510ef-07ed-44d9-946c-347680b783ec · outbound

This paper cites Foreseeing recon- struction quality of gradient inversion: An optimization perspective,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Foreseeing recon- struction quality of gradient inversion: An optimization perspective,

Reference 43

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Observation 3d92b254-f2a4-46db-a262-6fcef742c038 · outbound

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

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Learning multiple layers of features from tiny images,

Reference 44

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Observation 0e56aac2-e1fc-4992-808f-46c762fa174b · outbound

This paper cites Caltech-256 object category dataset,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Caltech-256 object category dataset,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T10:19:52.672118Z

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Observation 96cc9723-5982-45f4-8b82-8c3e1adc67af · outbound

This paper cites Automated flower classification over a large number of classes,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Automated flower classification over a large number of classes,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:19:52.928273Z

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Observation 6e4be033-04fc-49d1-8555-6d45b6a0ae64 · outbound

This paper cites Deep residual learning for image recognition,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Deep residual learning for image recognition,

Reference 47

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Observation 371d56c6-d5c9-4d86-8a9f-6d01c6afa227 · outbound

This paper cites Complex wavelet structural similarity: A new image similarity index,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Complex wavelet structural similarity: A new image similarity index,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:19:52.901647Z

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Observation 0d6ab166-b864-43f1-997d-e0e684aa68e6 · outbound

This paper cites Preserving privacy and security in federated learning,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Preserving privacy and security in federated learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:19:52.886044Z

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Observation 330a8e03-f5b7-470d-8eb9-03afd9477929 · outbound

This paper cites L-secnet: Towards secure and lightweight deep neural network inference,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy L-secnet: Towards secure and lightweight deep neural network inference,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:19:52.871024Z

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Observation 730bb7e7-3c48-4543-bb1d-bd1d8f20cd76 · outbound

This paper cites A convnet for the 2020s,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy A convnet for the 2020s,

Reference 51

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Observation b1226d00-b548-4681-80cb-1eb83592785a · outbound

This paper cites Densely connected convolutional networks,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Densely connected convolutional networks,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:19:52.841484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 07d54d27-0e36-4d6c-87eb-0f4b527bb3d9 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Efficientnet: Rethinking model scaling for convolutional neural networks,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T10:19:52.707539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07c8b4eb-c96b-4ce8-b894-15ba33caa9ec · outbound

This paper cites Going deeper with convolutions,.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy Going deeper with convolutions,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:19:52.817351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0aa55292-9fbb-4df4-9c06-8d6d90e5f769 · outbound

This paper cites She is currently a Senior Lecturer with the University of New South Wales, Canberra Campus, Australia.

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy She is currently a Senior Lecturer with the University of New South Wales, Canberra Campus, Australia

Reference 2018

Resolution: malformed identifier; raw_fallback, observed 2026-08-08T10:19:52.801935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers