iDLG: Improved Deep Leakage from Gradients | Paper Citation Record

Observation aa6bd328-f1c1-4768-9d5c-19023c010712 · inbound

Approximate and Weighted Data Reconstruction Attack in Federated Learning cites this paper.

Approximate and Weighted Data Reconstruction Attack in Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-24T07:24:06.143857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e516d988-8ca5-4b0e-9255-920b1ccc582c · inbound

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions cites this paper.

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions iDLG: Improved Deep Leakage from Gradients

Reference 187

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:48:39.192081Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d4d6a573-0931-49a7-b31c-4f6c06093af6 · inbound

Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning cites this paper.

Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T14:45:30.276038Z

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Observation 3858fcdf-b49f-4b38-bf5b-9625092bab90 · inbound

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients cites this paper.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients iDLG: Improved Deep Leakage from Gradients

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:10.273141Z

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Observation c30ea56c-d520-4736-9b21-946612ad662c · inbound

Lightweight Federated Learning with Differential Privacy and Straggler Resilience cites this paper.

Lightweight Federated Learning with Differential Privacy and Straggler Resilience iDLG: Improved Deep Leakage from Gradients

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T20:04:51.556545Z

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Observation a0c2cff6-448d-4d14-ab18-229c9139c140 · inbound

Membership Inference Attacks and Defenses in Federated Learning: A Survey cites this paper.

Membership Inference Attacks and Defenses in Federated Learning: A Survey iDLG: Improved Deep Leakage from Gradients

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T20:00:19.403628Z

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Observation e458707d-53ff-48b0-b24a-86274ade0241 · inbound

A New Federated Learning Framework Against Gradient Inversion Attacks cites this paper.

A New Federated Learning Framework Against Gradient Inversion Attacks iDLG: Improved Deep Leakage from Gradients

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T19:08:54.628478Z

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Observation 9ba668d7-faab-4215-917d-ef2d0ef46736 · inbound

Training Data Reconstruction: Privacy due to Uncertainty? cites this paper.

Training Data Reconstruction: Privacy due to Uncertainty? iDLG: Improved Deep Leakage from Gradients

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:55.044279Z

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Observation c7eb7356-46d7-4644-9ed4-8e173f384559 · inbound

GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning cites this paper.

GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T13:59:14.437789Z

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Observation 677ddfc3-fda7-4c12-b958-e11272e7dcda · inbound

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation cites this paper.

Fed-AugMix: Balancing Privacy and Utility via Data Augmentation iDLG: Improved Deep Leakage from Gradients

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T12:49:37.527741Z

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Observation 582ffb94-32d2-48bb-a54d-9f7cfe1c0f36 · inbound

FedGIG: Graph Inversion from Gradient in Federated Learning cites this paper.

FedGIG: Graph Inversion from Gradient in Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T04:45:50.873901Z

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Observation 6635b7c8-5575-4236-a654-f74e57eadfe7 · inbound

A Survey of Secure Semantic Communications cites this paper.

A Survey of Secure Semantic Communications iDLG: Improved Deep Leakage from Gradients

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-10T22:44:11.475973Z

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Observation 8a484179-2747-4884-b7cf-2f808e1d4cfd · inbound

Decoupled SGDA for Games with Intermittent Strategy Communication cites this paper.

Decoupled SGDA for Games with Intermittent Strategy Communication iDLG: Improved Deep Leakage from Gradients

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T15:06:09.184625Z

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Observation 6449b9c6-659e-4ff6-a9da-a5e9234fdb9e · inbound

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling cites this paper.

CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling iDLG: Improved Deep Leakage from Gradients

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T14:05:45.463438Z

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Observation dea2cfe7-f247-4b09-a536-660e46a09e57 · inbound

Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey cites this paper.

Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey iDLG: Improved Deep Leakage from Gradients

Reference 187

Resolution: unresolved; no resolver link, observed 2026-08-09T21:58:42.705162Z

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Observation 9259e078-b24d-4a83-8f2a-2fb6509493fd · inbound

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage cites this paper.

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage iDLG: Improved Deep Leakage from Gradients

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T10:44:58.547558Z

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Observation 367010bd-da7f-44ca-bf99-55597acc56c7 · inbound

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing cites this paper.

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing iDLG: Improved Deep Leakage from Gradients

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:19.213773Z

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Observation 5e7b6d6c-2e8b-444c-8839-948424f204b4 · inbound

Comparing privacy notions for protection against reconstruction attacks in machine learning cites this paper.

Comparing privacy notions for protection against reconstruction attacks in machine learning iDLG: Improved Deep Leakage from Gradients

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T23:53:24.221834Z

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Observation 8ebbd8c0-ccaf-4a72-85b1-baf02330bff0 · inbound

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning cites this paper.

The Gradient Puppeteer: Adversarial Domination in Gradient Leakage Attacks through Model Poisoning iDLG: Improved Deep Leakage from Gradients

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T23:33:38.862873Z

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Observation 8bf01601-721e-4bda-879e-3f1eea451019 · inbound

FedRE: Robust and Effective Federated Learning with Privacy Preference cites this paper.

FedRE: Robust and Effective Federated Learning with Privacy Preference iDLG: Improved Deep Leakage from Gradients

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:16.551840Z

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Observation 5f8c32ee-06d8-43af-b4d1-5c4074b33c76 · inbound

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning cites this paper.

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T21:20:18.269198Z

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Observation 4a081491-2b1c-4d4d-9de4-4aeab883776e · inbound

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption cites this paper.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption iDLG: Improved Deep Leakage from Gradients

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T15:34:38.508551Z

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Observation 8811c431-f7e2-4060-bf9c-a28a1f764a86 · inbound

LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments cites this paper.

LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments iDLG: Improved Deep Leakage from Gradients

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:13:34.161138Z

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Observation c339bb25-a87d-48e9-9333-c7eea890e729 · inbound

Gradient Inversion Transcript: Leveraging Robust Generative Priors to Reconstruct Training Data from Gradient Leakage cites this paper.

Gradient Inversion Transcript: Leveraging Robust Generative Priors to Reconstruct Training Data from Gradient Leakage iDLG: Improved Deep Leakage from Gradients

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:27.811626Z

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Observation eef6faaf-4576-429d-a835-9479a35b850f · inbound

Label Leakage in Federated Inertial-based Human Activity Recognition cites this paper.

Label Leakage in Federated Inertial-based Human Activity Recognition iDLG: Improved Deep Leakage from Gradients

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:11.352979Z

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Observation f52c5cfd-d0a2-4684-aad2-ec3a2f3a5dd0 · inbound

Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models cites this paper.

Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models iDLG: Improved Deep Leakage from Gradients

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:09.623844Z

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Observation 1bce2871-dfe9-49a9-b72d-4540451298e8 · inbound

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems cites this paper.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems iDLG: Improved Deep Leakage from Gradients

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:15.909897Z

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Observation 346203f9-2537-48b2-80d9-08013fda0c07 · inbound

Secure Distributed Learning for CAVs: Defending Against Gradient Leakage with Leveled Homomorphic Encryption cites this paper.

Secure Distributed Learning for CAVs: Defending Against Gradient Leakage with Leveled Homomorphic Encryption iDLG: Improved Deep Leakage from Gradients

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:26:50.125905Z

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Observation 5d98bd3f-28a8-4af1-a2fa-5b5867e4f17f · inbound

Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings cites this paper.

Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings iDLG: Improved Deep Leakage from Gradients

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T05:21:07.883060Z

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Observation 3353c9c9-5495-4978-a5f3-d677f1cea5a3 · inbound

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates cites this paper.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates iDLG: Improved Deep Leakage from Gradients

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:46.220200Z

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Observation 9a2ba5da-c2ec-4b9e-b4e6-12faee81ae17 · inbound

ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning cites this paper.

ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning iDLG: Improved Deep Leakage from Gradients

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T19:51:57.363769Z

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Observation fe7f97c7-75a7-449c-9723-6370019b950f · inbound

Shadow defense against gradient inversion attack in federated learning cites this paper.

Shadow defense against gradient inversion attack in federated learning iDLG: Improved Deep Leakage from Gradients

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:21:30.616801Z

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Observation 3c2b4421-646b-4a62-b9a1-e685250f09c9 · inbound

Topology-Aware Differential Privacy in Hierarchical Federated Learning cites this paper.

Topology-Aware Differential Privacy in Hierarchical Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:46:03.203888Z

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Observation 7cf528ff-6330-4622-8d9f-48701264221b · inbound

Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning cites this paper.

Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T22:46:42.081034Z

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Observation 793c06aa-fdab-4f55-a089-40ecff933035 · inbound

DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences cites this paper.

DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences iDLG: Improved Deep Leakage from Gradients

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:02.131700Z

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Observation b48b962c-03c8-4aee-8e83-9bc5f5db3f29 · inbound

Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks cites this paper.

Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks iDLG: Improved Deep Leakage from Gradients

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T13:56:22.585335Z

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Observation 701a94a3-0701-4005-a4fa-74193cdeacf1 · inbound

Uncovering Gradient Inversion Risks in Practical Language Model Training cites this paper.

Uncovering Gradient Inversion Risks in Practical Language Model Training iDLG: Improved Deep Leakage from Gradients

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T17:45:36.612674Z

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Observation 8b6b1013-0c0b-4f4f-8733-ef2009a49fbb · inbound

Hypernetworks for Model-Heterogeneous Personalized Federated Learning cites this paper.

Hypernetworks for Model-Heterogeneous Personalized Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T11:55:31.884037Z

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Observation cac595b2-1d42-4488-8aa0-59d57798c100 · inbound

Evaluating the Dynamics of Membership Privacy in Deep Learning cites this paper.

Evaluating the Dynamics of Membership Privacy in Deep Learning iDLG: Improved Deep Leakage from Gradients

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T10:58:06.094314Z

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Observation faf0b1c4-2330-4172-9e44-d54049a5677f · inbound

Label Inference Attacks against Federated Unlearning cites this paper.

Label Inference Attacks against Federated Unlearning iDLG: Improved Deep Leakage from Gradients

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T22:38:58.747371Z

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Observation e6b81c76-7d15-4198-abdf-1bc4dfe94fe5 · inbound

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives cites this paper.

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives iDLG: Improved Deep Leakage from Gradients

Reference 267

Resolution: unresolved; no resolver link, observed 2026-08-05T18:12:38.412145Z

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Observation 185ded53-f506-4cd6-b3f4-6b941656c205 · inbound

Sketched Gaussian Mechanism for Private Federated Learning cites this paper.

Sketched Gaussian Mechanism for Private Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T21:12:52.224737Z

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Observation b9b00dbc-8db5-447d-83b0-86992fbdb8e6 · inbound

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection cites this paper.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection iDLG: Improved Deep Leakage from Gradients

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T19:11:57.447593Z

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Observation e4cd18d0-5e29-4f00-80fc-fe3627baa527 · inbound

Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learning cites this paper.

Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learning iDLG: Improved Deep Leakage from Gradients

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T19:43:59.954962Z

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Observation 4c3968b7-585c-419b-a899-7f3b2993ed2e · inbound

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs cites this paper.

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs iDLG: Improved Deep Leakage from Gradients

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:30:51.355928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c3226fbf-8237-45ef-bfd7-1b73afd9a571 · inbound

Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation cites this paper.

Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation iDLG: Improved Deep Leakage from Gradients

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:51:11.037065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 385fe5d8-4359-4a33-a158-5cbc609cd490 · inbound

Federated User Behavior Modeling for Privacy-Preserving LLM Recommendation cites this paper.

Federated User Behavior Modeling for Privacy-Preserving LLM Recommendation iDLG: Improved Deep Leakage from Gradients

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T10:09:06.489828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f9b0f2d4-1ef0-4c67-b67b-9b687ca25efb · inbound

SafeLM: Unified Privacy-Aware Optimization for Trustworthy Federated Large Language Models cites this paper.

SafeLM: Unified Privacy-Aware Optimization for Trustworthy Federated Large Language Models iDLG: Improved Deep Leakage from Gradients

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:13:30.504722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation efd10b79-1019-473d-b0dd-6964dcada28c · inbound

UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment cites this paper.

UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment iDLG: Improved Deep Leakage from Gradients

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:18.040297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 50bb0aff-8a95-4543-b798-49af401145be · inbound

On What We Can Learn from Low-Resolution Data cites this paper.

On What We Can Learn from Low-Resolution Data iDLG: Improved Deep Leakage from Gradients

Reference 110

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:32:19.164126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 81a4fa43-e5d3-4c2c-baf0-5704d49dc0bc · inbound

LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections cites this paper.

LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections iDLG: Improved Deep Leakage from Gradients

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:47:52.990549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0ca3d2a7-390f-4863-b145-bd400d023e18 · inbound

Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems cites this paper.

Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems iDLG: Improved Deep Leakage from Gradients

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T19:33:42.207110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 60ffd0f9-63f1-4ce7-866e-701576ab1419 · inbound

FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning cites this paper.

FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:36:40.202704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 10aa6f53-284f-4adf-9993-4592a93cd483 · inbound

Local Differential Privacy via Dynamic Quantization in Distributed Online Stochastic Optimization cites this paper.

Local Differential Privacy via Dynamic Quantization in Distributed Online Stochastic Optimization iDLG: Improved Deep Leakage from Gradients

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-06-29T06:03:08.817359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2de3c98b-a564-4680-874e-5680ae861a25 · inbound

Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning cites this paper.

Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:46:14.123181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ad1ddc5c-4c16-4e20-9ff5-ed145e987289 · inbound

DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data cites this paper.

DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data iDLG: Improved Deep Leakage from Gradients

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:06:41.143091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cb47a329-1a50-4a90-953c-32bcc9d9afb7 · inbound

Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning cites this paper.

Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning iDLG: Improved Deep Leakage from Gradients

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-03T07:57:44.288500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a397fed1-909a-49ef-b449-80e4100ba73c · inbound

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs cites this paper.

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs iDLG: Improved Deep Leakage from Gradients

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:38:43.793108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9a143d38-99c9-41b0-8331-44697dd203ff · inbound

TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization cites this paper.

TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization iDLG: Improved Deep Leakage from Gradients

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:28:59.420722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 77516e72-8a29-4c82-a1c2-5b34ce6550c1 · inbound

From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning cites this paper.

From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning iDLG: Improved Deep Leakage from Gradients

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:39:35.034868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6d5ae6e5-03f0-49cf-9d28-130a677145c3 · inbound

HADES: Privacy-Preserving Federated Learning via Selective Feature Encryption and Hybrid Model Fusion cites this paper.

HADES: Privacy-Preserving Federated Learning via Selective Feature Encryption and Hybrid Model Fusion iDLG: Improved Deep Leakage from Gradients

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:29:50.211214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d5f9e80f-a1ac-4a49-9589-37f7fdca070c · inbound

Exposing the Illusion of Erasure in Knowledge Editing for LLMs cites this paper.

Exposing the Illusion of Erasure in Knowledge Editing for LLMs iDLG: Improved Deep Leakage from Gradients

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:09:44.315325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8926ec3c-7c16-4cf8-b2cb-1951cd5d2b34 · inbound

TL++: Accuracy and Privacy Preserving Traversal Learning for Distributed Intelligent Systems cites this paper.

TL++: Accuracy and Privacy Preserving Traversal Learning for Distributed Intelligent Systems iDLG: Improved Deep Leakage from Gradients

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:50:10.991604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5a613271-5da4-4c01-8989-f05ded997bc7 · inbound

FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation cites this paper.

FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation iDLG: Improved Deep Leakage from Gradients

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T14:36:17.470325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bf6ba427-6506-4db5-adca-b951ec2a362f · inbound

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks cites this paper.

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks iDLG: Improved Deep Leakage from Gradients

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T02:54:02.313861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a888b35-fc3b-44f4-9893-9393b8b71ddf · inbound

Code-Poisoning Property Inference Attacks cites this paper.

Code-Poisoning Property Inference Attacks iDLG: Improved Deep Leakage from Gradients

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T21:49:07.116513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64843254-88f2-48b7-813a-5633b11b36d3 · inbound

BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakage cites this paper.

BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakage iDLG: Improved Deep Leakage from Gradients

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-31T11:46:52.426745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7817562-e24f-4f78-b3d2-1de6a2c33671 · inbound

Don't Trust the AI Ecosystem: Analyzing Privacy Leakage in Compromised Open-Source Components cites this paper.

Don't Trust the AI Ecosystem: Analyzing Privacy Leakage in Compromised Open-Source Components iDLG: Improved Deep Leakage from Gradients

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-31T23:40:03.750019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2005e9a-e3f1-4669-a8a7-0e3de04654c5 · inbound

Don't Trust the AI Ecosystem: Analyzing Privacy Leakage in Compromised Open-Source Components cites this paper.

Don't Trust the AI Ecosystem: Analyzing Privacy Leakage in Compromised Open-Source Components iDLG: Improved Deep Leakage from Gradients

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T01:44:07.275956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f0b3a3f-445c-4ef2-abc9-c8127fb0c319 · inbound

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement cites this paper.

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement iDLG: Improved Deep Leakage from Gradients

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T22:47:13.303660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bf33032-04ef-4787-816a-35c64bf3ee88 · inbound

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement cites this paper.

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement iDLG: Improved Deep Leakage from Gradients

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T01:42:09.787047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers