PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction | Paper Citation Record

Observation 0dacc60d-bf81-4978-8976-21e98fc28f99 · outbound

This paper cites Image Reconstruction Using Deep Learning.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Image Reconstruction Using Deep Learning

Reference 1

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Observation 74eb0081-c2c7-454a-91b2-ed39a1bd985d · outbound

This paper cites Deep convolutional autoencoder-based lossy image compression,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Deep convolutional autoencoder-based lossy image compression,

Reference 2

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This paper cites Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective,

Reference 3

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Observation c852fdde-02cf-404e-9770-55d8a8420eab · outbound

This paper cites Centralized machine learning versus federated averaging: A comparison using mnist dataset,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Centralized machine learning versus federated averaging: A comparison using mnist dataset,

Reference 4

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Observation 56e2d40c-a9f1-4cbd-bade-c7ea79f83f86 · outbound

This paper cites Recent advances of differen- tial privacy in centralized deep learning: A systematic survey,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Recent advances of differen- tial privacy in centralized deep learning: A systematic survey,

Reference 5

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Observation c3df0d27-1053-4639-a311-24bf9ba91a31 · outbound

This paper cites Gradient centralization: A new optimization technique for deep neural networks,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Gradient centralization: A new optimization technique for deep neural networks,

Reference 6

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Observation 5d06fbf0-f389-4dd1-8eee-b608bbd19e31 · outbound

This paper cites Federated Learning Versus Classical Machine Learning: A Convergence Comparison.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated Learning Versus Classical Machine Learning: A Convergence Comparison

Reference 7

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Observation 282c1168-cb32-4cb2-8eb6-69820e3befa6 · outbound

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

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Communication-efficient learning of deep networks from decentralized data,

Reference 8

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Observation 096fed5d-3bf7-4371-b30f-6e87dd8e067e · outbound

This paper cites Differential privacy,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Differential privacy,

Reference 9

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Observation d27e8d63-dbc4-497c-b9a3-bab9395bed28 · outbound

This paper cites Differential privacy: A survey of results,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Differential privacy: A survey of results,

Reference 10

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Observation d07f5511-8de5-4a86-bc68-e5043e7b1a41 · outbound

This paper cites Boosting and differential privacy,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Boosting and differential privacy,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.948666Z

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Observation ac203af8-d723-4053-88e1-20c7d7655a51 · outbound

This paper cites Gaussian differential privacy,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Gaussian differential privacy,

Reference 12

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This paper cites an unresolved cited work.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Unresolved cited work

Reference 13

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Observation 8fbca128-80a8-4ab7-8061-fa551479ad9e · outbound

This paper cites A survey on homomorphic encryption schemes: Theory and implementation,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction A survey on homomorphic encryption schemes: Theory and implementation,

Reference 14

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Observation 382d0e8b-33ea-4cb1-af7b-bc9855037236 · outbound

This paper cites Homomorphic encryption,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Homomorphic encryption,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.924877Z

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Observation 5f5e6ee7-97fb-443e-ab35-e75764df04a4 · outbound

This paper cites Somewhat practical fully homomorphic encryption,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Somewhat practical fully homomorphic encryption,

Reference 16

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Observation 87e6ad6d-1f61-417d-9653-c3ed888274d7 · outbound

This paper cites Cramer, I.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Cramer, I

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.911548Z

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Observation 0de2a778-2231-4413-a573-0959097fc078 · outbound

This paper cites Secure multiparty computation,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Secure multiparty computation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.903035Z

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Observation 61b8b059-c3c2-4be3-99b6-18b8e51caf54 · outbound

This paper cites Secure multi-party computation,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Secure multi-party computation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.895032Z

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Observation aa919d1f-56a3-4fe0-a258-a48f18f474ac · outbound

This paper cites A novel encryption-then-lossy-compression scheme of color images using customized residual dense spatial network,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction A novel encryption-then-lossy-compression scheme of color images using customized residual dense spatial network,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.887220Z

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Observation 0b4673bc-5289-42cc-8c63-b87ef5ede2e5 · outbound

This paper cites Lossy compression and iterative reconstruction for encrypted image,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Lossy compression and iterative reconstruction for encrypted image,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.879359Z

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Observation bb861ab5-b9ac-4eae-8750-6540b65d2a01 · outbound

This paper cites A new lossy compression scheme for encrypted gray-scale images,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction A new lossy compression scheme for encrypted gray-scale images,

Reference 22

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Observation 08931842-93ce-43c2-981c-1b1918582a0d · outbound

This paper cites Encryption-then- compression systems using grayscale-based image encryption for jpeg images,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Encryption-then- compression systems using grayscale-based image encryption for jpeg images,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.863111Z

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Observation f436366c-138a-47ad-80e6-aeb3795bab8f · outbound

This paper cites Compressive sampling and lossy compression,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Compressive sampling and lossy compression,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.855429Z

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Observation 9982c905-91ae-4b37-b841-ad0ed2dc92e8 · outbound

This paper cites Lossy compression of noisy images,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Lossy compression of noisy images,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.847025Z

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Observation a4eabb67-5678-4206-89f3-5b1a4f846eb7 · outbound

This paper cites Rethinking lossy compression: The rate- distortion-perception tradeoff,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Rethinking lossy compression: The rate- distortion-perception tradeoff,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.838854Z

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Observation 9755cb03-4884-4f6a-a919-455181ce1fb7 · outbound

This paper cites Residual dense net- work for image super-resolution,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Residual dense net- work for image super-resolution,

Reference 27

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Observation 7ec3da86-6d77-480c-abd3-1b97f3f6c0b8 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction U-net: Convolutional networks for biomedical image segmentation,

Reference 28

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Observation a9f48778-f5e5-4935-a8f4-4f63f56cabaa · outbound

This paper cites Theory of deep convolutional neural networks: Downsam- pling,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Theory of deep convolutional neural networks: Downsam- pling,

Reference 29

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Observation bcac2a3e-d623-437e-8910-e840487380dc · outbound

This paper cites An encryption-then-compression system for jpeg standard,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction An encryption-then-compression system for jpeg standard,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.808724Z

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Observation adc98a28-de27-49c4-aa3e-d80dea7b87cf · outbound

This paper cites Federated learning: Challenges, methods, and future directions,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated learning: Challenges, methods, and future directions,

Reference 31

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Observation 720f261d-2c49-4abd-84f4-489b82148057 · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction On the Convergence of FedAvg on Non-IID Data

Reference 32

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Observation f0416c19-5985-4035-a7cf-6d388a394fd6 · outbound

This paper cites Revisiting Distributed Synchronous SGD.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Revisiting Distributed Synchronous SGD

Reference 33

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Observation db6c4580-a4c8-4969-9ff3-eef76df367d1 · outbound

This paper cites Asynchronous decentralized parallel stochastic gradient descent,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Asynchronous decentralized parallel stochastic gradient descent,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.794856Z

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Observation 0cf44c6e-3d3d-4ae5-b81c-563f800a6bf2 · outbound

This paper cites Federated learning with hierarchical clustering of local updates to improve training on non-iid data,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated learning with hierarchical clustering of local updates to improve training on non-iid data,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.786852Z

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Observation 6446c4d4-a7bc-4523-956f-2d707c8622e4 · outbound

This paper cites Timely communication in federated learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Timely communication in federated learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.778592Z

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Observation a39efb73-8ef7-4577-b19f-f785bc68de78 · outbound

This paper cites Federated Learning: Opportunities and Challenges.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated Learning: Opportunities and Challenges

Reference 37

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Observation 79564c45-2c35-45eb-b12c-8d3a82b1ab55 · outbound

This paper cites How to backdoor federated learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction How to backdoor federated learning,

Reference 38

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Observation 4ecdfaa9-8d28-4258-a687-fec205ad6393 · outbound

This paper cites A survey on federated learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction A survey on federated learning,

Reference 39

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Observation b183339d-d289-4caf-a0c7-8b13957f547c · outbound

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

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated Learning: Strategies for Improving Communication Efficiency

Reference 40

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Observation d1b3e673-c431-4cb1-9932-c2a3cb209c01 · outbound

This paper cites Federated learning on non-iid data: A survey,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated learning on non-iid data: A survey,

Reference 41

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Observation 73ba0dd2-0dcf-407e-86e9-518d7e4bbe8f · outbound

This paper cites Threats to Federated Learning: A Survey.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Threats to Federated Learning: A Survey

Reference 42

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Observation 6ffd2972-74a2-4b56-b4ae-fb13ba0b4b6e · outbound

This paper cites Communication-efficient federated learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Communication-efficient federated learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.755255Z

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Observation 21f640e5-786b-46d9-97b9-ba7de8dc5e0e · outbound

This paper cites Federated learning for internet of things: Recent advances, taxonomy, and open challenges,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated learning for internet of things: Recent advances, taxonomy, and open challenges,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.746989Z

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Observation d1e1374c-89ba-4925-8eb6-32f947901c3a · outbound

This paper cites Specificity- preserving federated learning for mr image reconstruction,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Specificity- preserving federated learning for mr image reconstruction,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.738318Z

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Observation fcb69236-a0e9-4dc6-8469-74a1bf38de50 · outbound

This paper cites Federated learning of generative image priors for mri reconstruction,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Federated learning of generative image priors for mri reconstruction,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.729950Z

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Observation ae4efb95-0e87-4fbf-acc4-de184e8b818c · outbound

This paper cites Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.720884Z

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Observation ccb2352e-7ac4-4065-9b33-f704cf89978f · outbound

This paper cites DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:27:06.069543Z

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Observation e3103031-0804-4672-aeee-e4d4bb02ee83 · outbound

This paper cites Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:27:06.010515Z

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Observation ae50705f-27a3-49c9-8f4e-f8af6acc4af1 · outbound

This paper cites Adaptive laplace mechanism: Differential privacy preservation in deep learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Adaptive laplace mechanism: Differential privacy preservation in deep learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.712875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09c17e7e-7521-4044-94a7-c1af894a1795 · outbound

This paper cites Secure random sampling in differential privacy,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Secure random sampling in differential privacy,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.704243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 473aecf2-621a-42a7-8fde-93e3b3dae78f · outbound

This paper cites On significance of the least significant bits for differential privacy,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction On significance of the least significant bits for differential privacy,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.561239Z

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Observation 95c781a9-e749-468a-ada3-52fb63dfbaa2 · outbound

This paper cites Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computers.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computers

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.564631Z

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Unavailable: canonical work link unavailable.

Observation 17b21068-0e45-4c92-afdc-84a4fcefc7f9 · outbound

This paper cites Widespread underestimation of sensitivity in differentially private libraries and how to fix it,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Widespread underestimation of sensitivity in differentially private libraries and how to fix it,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.567433Z

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Unavailable: canonical work link unavailable.

Observation e8f431e5-db7e-4432-a3f8-f9cbeaf420c4 · outbound

This paper cites Practical black-box attacks against machine learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Practical black-box attacks against machine learning,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.569926Z

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Observation e6c00631-6cef-4b95-b0bc-178d93b2549d · outbound

This paper cites How to prove yourself: Practical solutions to identification and signature problems,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction How to prove yourself: Practical solutions to identification and signature problems,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.680509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f7f1d571-8fc6-46a3-a479-a4a5e9df789c · outbound

This paper cites Membership Inference Attacks against Machine Learning Models.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Membership Inference Attacks against Machine Learning Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.575576Z

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Observation 3fe8e685-e944-4289-9754-2d5e3a1d03a6 · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Model inversion attacks that exploit confidence information and basic countermeasures,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.578579Z

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Observation 2393d2e2-1c4a-43bf-85b3-e0af6790ba2b · outbound

This paper cites Property inference attacks on fully connected neural networks using permutation invariant representations,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Property inference attacks on fully connected neural networks using permutation invariant representations,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.581663Z

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Observation ca565e66-5a7e-4d9f-ba5a-e538d2099360 · outbound

This paper cites The algorithmic foundations of differential privacy,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction The algorithmic foundations of differential privacy,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.584647Z

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Observation 577fa739-7b0b-442e-a255-d5bc2554f91b · outbound

This paper cites Advances and open problems in federated learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Advances and open problems in federated learning,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.588091Z

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Observation f80b7568-1c58-438a-9b23-ac3ba2a5968b · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.591497Z

Source-reported events for the cited work

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Observation e9e9abc4-8875-4e6a-9d5d-f8c3b60c6e51 · outbound

This paper cites How To Backdoor Federated Learning.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction How To Backdoor Federated Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.594480Z

Source-reported events for the cited work

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Observation 803a054d-40ed-43ca-8ad4-e0d74b4336e0 · outbound

This paper cites Local differential privacy for deep learning,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Local differential privacy for deep learning,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.651121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cc39ec26-0dc0-41b4-9cc3-2a4385f049e9 · outbound

This paper cites Ldp-fed: Federated learning with local differential privacy,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Ldp-fed: Federated learning with local differential privacy,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.642940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8655521f-ea0d-4348-90b3-9ad7c7018f87 · outbound

This paper cites Privacy-preserving face recognition with learnable privacy budgets in frequency domain,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Privacy-preserving face recognition with learnable privacy budgets in frequency domain,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.634006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e30ebdb6-cdf1-4695-a8ae-9b511cdbcd6a · outbound

This paper cites Privacy preserving face recognition utilizing differential privacy,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Privacy preserving face recognition utilizing differential privacy,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.624925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d6312d7-e3d5-48e5-866f-c10fa1df1068 · outbound

This paper cites Embedding watermarks into deep neural networks,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Embedding watermarks into deep neural networks,

Reference 68

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:27:05.837625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6c9244b3-3c47-4265-88bd-d59f0c7ff15b · outbound

This paper cites Robust watermarking for deep neural networks via bi-level optimization,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Robust watermarking for deep neural networks via bi-level optimization,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.616322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5323b65c-6dc6-4080-9b75-a7fc80465939 · outbound

This paper cites Embedding water- marks into deep neural networks,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Embedding water- marks into deep neural networks,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.614344Z

Source-reported events for the cited work

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Observation c10a341d-8f57-497b-92ad-13912d34d430 · outbound

This paper cites A systematic review on model watermarking for neural networks,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction A systematic review on model watermarking for neural networks,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.603643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4f74c2f-9eb0-4164-96e7-68ed4d0f2b14 · outbound

This paper cites Fedipr: Ownership verification for federated deep neural network models,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Fedipr: Ownership verification for federated deep neural network models,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.595194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2924dfb0-ea4a-423e-adb4-d664a58ab759 · outbound

This paper cites A downsampling method addressing the modifiable areal unit problem in remote sensing,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction A downsampling method addressing the modifiable areal unit problem in remote sensing,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.586225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 338df136-f224-4ce4-9b96-bbda16078be5 · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.626738Z

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Observation 845f4514-7eac-4b35-9f5f-5568cbe01baa · outbound

This paper cites Adam: A method for stochastic optimization,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Adam: A method for stochastic optimization,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.629737Z

Source-reported events for the cited work

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Observation 2c5f63cc-5078-409a-ad09-ddb53fdf05d4 · outbound

This paper cites Enhancing communication efficiency and training time uniformity in federated learning through multi-branch networks and the oort algorithm,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Enhancing communication efficiency and training time uniformity in federated learning through multi-branch networks and the oort algorithm,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.572625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9db80cab-23b0-40b4-a411-27e2f1fc09fe · outbound

This paper cites Chacha20 and poly1305 for ietf protocols,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Chacha20 and poly1305 for ietf protocols,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:05.638883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d17dedd5-c973-487e-ad50-27af5936c70e · outbound

This paper cites Hybrid public key encryption (hpke),.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Hybrid public key encryption (hpke),

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.550312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b06bf46b-c5b5-4a0e-9c7c-cae0e9b5f3a3 · outbound

This paper cites The messaging layer security (mls) protocol,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction The messaging layer security (mls) protocol,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.542405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9698a077-1401-4b17-8822-b552f7c4d861 · outbound

This paper cites The x3dh key agreement protocol,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction The x3dh key agreement protocol,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.526194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3cd07067-d24f-4afc-a4af-6a9065e03aaa · outbound

This paper cites Available: https://www.rfc-editor.org/rfc/rfc9420.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Available: https://www.rfc-editor.org/rfc/rfc9420

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.534428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f5b44025-a5d0-491e-9945-c3b017e65f8b · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Ntire 2017 challenge on single image super-resolution: Dataset and study,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.508877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e9dbd1e3-a26a-4981-880b-ff2f54eccf08 · outbound

This paper cites Divertible protocols and atomic proxy cryptography,.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Divertible protocols and atomic proxy cryptography,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.517865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 652fcb46-32d9-4f43-a5b4-20ff3abcab57 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 267234091.

PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction Available: https://api.semanticscholar.org/CorpusID: 267234091

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:27:06.564066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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