Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning | Paper Citation Record

Observation 6f6b65c3-f768-45c0-9319-253dd19934e4 · outbound

This paper cites Goodfellow, H.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Goodfellow, H

Reference 1

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Observation e2de3b2d-c116-48a3-9b1b-c0da2d7ac9e7 · outbound

This paper cites an unresolved cited work.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Unresolved cited work

Reference 2

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Observation 5a32beb4-18f2-4471-83f5-265d3908d71c · outbound

This paper cites QSGD: communication-efficient SGD via gradient quantization and encoding.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning QSGD: communication-efficient SGD via gradient quantization and encoding

Reference 3

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Observation e0ec741c-f351-4e52-81cd-0e8e07afd5f6 · outbound

This paper cites The con- vergence of sparsified gradient methods.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning The con- vergence of sparsified gradient methods

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.535222Z

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Observation 32e0af99-40b3-4a73-9e22-97866bf10b8e · outbound

This paper cites When the curious abandon honesty: Federated learning is not private.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning When the curious abandon honesty: Federated learning is not private

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.528640Z

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Observation c881627c-7884-4032-9550-87d48c37e5c3 · outbound

This paper cites Tinytl: Reduce memory, not parameters for efficient on-device learning.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Tinytl: Reduce memory, not parameters for efficient on-device learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.522066Z

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Observation e26e6572-1a6b-4f08-82b6-ed48fc7d1af1 · outbound

This paper cites Brown, Dawn Song, ´Ulfar Erlingsson, Alina Oprea, and Colin Raffel.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Brown, Dawn Song, ´Ulfar Erlingsson, Alina Oprea, and Colin Raffel

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.515214Z

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Observation 06380ba4-f6c2-4ebd-998d-37e6a70d51c9 · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recogni- tion.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Adaptformer: Adapting vision transformers for scalable visual recogni- tion

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.508824Z

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Observation b0f9c0be-1d31-4f10-86be-a42a994f57f8 · outbound

This paper cites The janus interface: How fine-tuning in large language models amplifies the privacy risks.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning The janus interface: How fine-tuning in large language models amplifies the privacy risks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.502194Z

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Observation abac8ffe-9502-42ed-b4a3-d244cdb932e6 · outbound

This paper cites Fowl, Micah Gold- blum, and Tom Goldstein.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Fowl, Micah Gold- blum, and Tom Goldstein

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.495541Z

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Observation d152c830-ff40-4156-94b0-5220e1349d29 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Imagenet: A large-scale hierarchical image database

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.489154Z

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Observation ed8f9e5b-289b-4a4a-8276-9502afcec776 · outbound

This paper cites Effi- cient adaptation of large vision transformer via adapter re- composing.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Effi- cient adaptation of large vision transformer via adapter re- composing

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.482394Z

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Observation e8c0fc1a-6df0-49e0-b480-c2f30721ec82 · outbound

This paper cites Low-rank rescaled vision transformer fine-tuning: A residual design approach.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Low-rank rescaled vision transformer fine-tuning: A residual design approach

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.475732Z

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Observation ced3fd0c-c446-4fe1-b2e7-1470a1106e49 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.468986Z

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Observation 00d60324-a0d6-4c29-8b48-1f670dedd445 · outbound

This paper cites GIFD: A generative gradient inversion method with fea- ture domain optimization.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning GIFD: A generative gradient inversion method with fea- ture domain optimization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.462186Z

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Observation 2e2615b1-e15d-4d15-a042-80dd5f10ceee · outbound

This paper cites Privacy backdoors: Stealing data with corrupted pretrained models.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Privacy backdoors: Stealing data with corrupted pretrained models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.455188Z

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Observation 35cad2b0-8d47-4722-b274-c2815360f3ae · outbound

This paper cites Fowl, Jonas Geiping, Wojciech Czaja, Micah Gold- blum, and Tom Goldstein.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Fowl, Jonas Geiping, Wojciech Czaja, Micah Gold- blum, and Tom Goldstein

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.445982Z

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Observation 219cbc82-57b5-4cb3-9a42-80973b86e410 · outbound

This paper cites Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, and Tom Goldstein.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, and Tom Goldstein

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.439236Z

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Observation d4aa687c-f975-49a7-8656-87a8085e4406 · outbound

This paper cites Practical membership inference attacks against fine-tuned large language models via self- prompt calibration.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Practical membership inference attacks against fine-tuned large language models via self- prompt calibration

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.432619Z

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Observation 0af4447d-20fa-48c8-8754-6847306363ef · outbound

This paper cites Inverting gradients - how easy is it to break privacy in federated learning? In Advances in Neural Infor- mation Processing Systems (NeurIPS), 2020.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Inverting gradients - how easy is it to break privacy in federated learning? In Advances in Neural Infor- mation Processing Systems (NeurIPS), 2020

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.425915Z

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Observation d119f9ea-cafa-4eb8-a651-75f9af46a5cf · outbound

This paper cites Gradvit: Gradi- ent inversion of vision transformers.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Gradvit: Gradi- ent inversion of vision transformers

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.419316Z

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Observation fcc996b1-45d0-435b-91c1-b917fb77f002 · outbound

This paper cites Gaussian error linear units (gelus).

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Gaussian error linear units (gelus)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.412559Z

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Observation 6002673b-4e80-436e-a887-092479cbc4d5 · outbound

This paper cites Parameter-efficient transfer learning for NLP.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Parameter-efficient transfer learning for NLP

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.405871Z

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Observation 15395447-c54f-4137-b1e3-bee5443c0ff0 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.399335Z

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Observation d95480e3-1720-4943-bbd1-0a9d174441df · outbound

This paper cites Evaluating gradient inversion attacks and de- fenses in federated learning.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Evaluating gradient inversion attacks and de- fenses in federated learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.392428Z

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Observation 8e2b9b01-b171-44c1-912f-603468a7b755 · outbound

This paper cites Gradient inversion with generative image prior.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Gradient inversion with generative image prior

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.385792Z

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Observation 178fc1d6-f0cc-4df4-931b-fafe41bca7ed · outbound

This paper cites Belongie, Bharath Hariharan, and Ser-Nam Lim.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Belongie, Bharath Hariharan, and Ser-Nam Lim

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.379296Z

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Observation d6eb02ef-2aa3-4c56-a776-ffe06d50bff1 · outbound

This paper cites Edward Suh, Moinuddin K.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Edward Suh, Moinuddin K

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.372648Z

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Observation e7de4cd1-6a98-41ee-92be-09f19a536f77 · outbound

This paper cites Client-customized adaptation for parameter-efficient federated learning.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Client-customized adaptation for parameter-efficient federated learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.366273Z

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Observation f529f671-d475-401d-a8f9-a68c9282bf42 · outbound

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

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Learning multiple layers of features from tiny images

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.359621Z

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Observation 7006b5ad-7348-4dac-b009-19b28f757692 · outbound

This paper cites Tiny imagenet visual recognition challenge.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Tiny imagenet visual recognition challenge

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.353067Z

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Observation c26c74b4-1514-42ca-9790-701eab266702 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning The power of scale for parameter-efficient prompt tuning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.346534Z

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Observation 408b35de-e095-4106-8d72-ab0006860a03 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Prefix-tuning: Optimizing continuous prompts for generation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.339824Z

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Observation f3000e97-37a2-4ed8-aca2-200f66762dfa · outbound

This paper cites Au- diting privacy defenses in federated learning via generative gradient leakage.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Au- diting privacy defenses in federated learning via generative gradient leakage

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.333287Z

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Observation c1a2c2b1-1d17-4c6c-bf3a-54202a2b5063 · outbound

This paper cites Deep gradient compression: Reducing the communication bandwidth for distributed training.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Deep gradient compression: Reducing the communication bandwidth for distributed training

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.326706Z

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Observation 07bb609f-a879-439b-97ba-aede2daac737 · outbound

This paper cites Pre- curious: How innocent pre-trained language models turn into privacy traps.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Pre- curious: How innocent pre-trained language models turn into privacy traps

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.319986Z

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Observation c661ec25-074c-4600-9c5b-ec7f8de42863 · outbound

This paper cites APRIL: finding the achilles’ heel on privacy for vision transformers.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning APRIL: finding the achilles’ heel on privacy for vision transformers

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.313426Z

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Observation 7cc5817b-e555-4598-bde8-0c6d09188be1 · outbound

This paper cites Analyzing leak- age of personally identifiable information in language mod- els.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Analyzing leak- age of personally identifiable information in language mod- els

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.306776Z

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Observation d7f0bfc4-aba1-4c21-aa93-436b2ec40c70 · outbound

This paper cites Re- ducing communication overhead in federated learning for pre-trained language models using parameter-efficient fine- tuning.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Re- ducing communication overhead in federated learning for pre-trained language models using parameter-efficient fine- tuning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.299944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02fd6425-c1f9-4f24-8fa5-672073d0d7ed · outbound

This paper cites Mini but mighty: Finetuning vits with mini adapters.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Mini but mighty: Finetuning vits with mini adapters

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.292816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 690ea2c3-755f-43bd-8bd7-79daa54d5871 · outbound

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

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Communication- efficient learning of deep networks from decentralized data

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.285871Z

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Observation 255e54ba-3146-4ff6-9689-7a698c817e35 · outbound

This paper cites Shokri, and Amir Houmansadr.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Shokri, and Amir Houmansadr

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.279035Z

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Observation 9b0ede9b-b5a6-4ec5-93d5-0439cf30750a · outbound

This paper cites an unresolved cited work.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:08.272547Z

Source-reported events for the cited work

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Observation d07be10b-c399-4b37-979d-066524e23116 · outbound

This paper cites an unresolved cited work.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:08.265749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 703047d5-3b2e-414e-b5ce-c05dff38e21b · outbound

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

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Eluding secure aggregation in federated learning via model inconsistency

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.259162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f9db09f9-fec7-4c37-8f06-fb3697b6f19b · outbound

This paper cites Adapterhub: A framework for adapting transformers.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Adapterhub: A framework for adapting transformers

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.252377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ca5b032f-a6e9-4e68-81fe-612f0015c6ce · outbound

This paper cites an unresolved cited work.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:08.245537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bf233fbf-95ae-4ce6-96b5-d5cf349c384f · outbound

This paper cites Dropout is NOT all you need to prevent gradient leakage.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Dropout is NOT all you need to prevent gradient leakage

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.238837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f41e7b93-65a3-4e67-abb9-ef5cd61f513c · outbound

This paper cites Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.232065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec93c9f3-8e1c-4bf6-80a1-e42b62dd6adc · outbound

This paper cites an unresolved cited work.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:08.225301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e9b4237b-ce64-49b8-be85-a5a8ada57588 · outbound

This paper cites Systematic evaluation of pri- vacy risks of machine learning models.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Systematic evaluation of pri- vacy risks of machine learning models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.218784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f4f00a3-6816-4c7d-bb37-27ac323dee95 · outbound

This paper cites Im- proving lora in privacy-preserving federated learning.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Im- proving lora in privacy-preserving federated learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.211917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 99e1c1d8-6b65-48d3-be0b-545990a72a97 · outbound

This paper cites VL- ADAPTER: parameter-efficient transfer learning for vision- and-language tasks.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning VL- ADAPTER: parameter-efficient transfer learning for vision- and-language tasks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.205552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a84cbbf1-96ed-46b1-ab21-19b43364ca0e · outbound

This paper cites Manipulating transfer learning for property inference.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Manipulating transfer learning for property inference

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.198710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cf6c7000-cb58-4d49-8df8-b1eda35f34b7 · outbound

This paper cites Vu, Truc D.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Vu, Truc D

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.191359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d8a34e54-adab-49fa-8a46-081a5f54c421 · outbound

This paper cites Sheikh, and Eero P.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Sheikh, and Eero P

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.184733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5aa2ef3b-c25c-49b2-801a-d7a78afc8666 · outbound

This paper cites Pretrained models for multilingual federated learning.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Pretrained models for multilingual federated learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.177394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f248e0cf-5249-4b94-9e13-631f7a3b6ebc · outbound

This paper cites Batched low-rank adap- tation of foundation models.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Batched low-rank adap- tation of foundation models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.170421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eab3b880-e76c-430e-9af6-dd92e8f7ac9e · outbound

This paper cites Fishing for user data in large-batch fed- erated learning via gradient magnification.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Fishing for user data in large-batch fed- erated learning via gradient magnification

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.163516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 829664a6-c58f-4a1f-9b7b-f3b834d36580 · outbound

This paper cites Privacy back- doors: Enhancing membership inference through poisoning pre-trained models.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Privacy back- doors: Enhancing membership inference through poisoning pre-trained models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.156260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cd43c059-8913-488b-8580-8b161fb9729e · outbound

This paper cites Perada: Parameter-efficient federated learning personalization with generalization guarantees.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Perada: Parameter-efficient federated learning personalization with generalization guarantees

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.149039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5e3cdb84-ac07-438c-a339-8778c5ad03ca · outbound

This paper cites Efficient low-rank backprop- agation for vision transformer adaptation.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Efficient low-rank backprop- agation for vision transformer adaptation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.141732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4538e37d-b569-40de-970b-3d8498b0fecd · outbound

This paper cites an unresolved cited work.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:08.134066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 554f6bfe-28c4-444b-81e5-7fef685324ac · outbound

This paper cites How does a deep learning model architecture impact its privacy? A comprehensive study of privacy attacks on cnns and transformers.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning How does a deep learning model architecture impact its privacy? A comprehensive study of privacy attacks on cnns and transformers

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.126886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3aaa7e5d-d2cc-448e-9e5c-a1853442ec8c · outbound

This paper cites Efros, Eli Shecht- man, and Oliver Wang.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Efros, Eli Shecht- man, and Oliver Wang

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.118327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f708e7ad-33e9-4d27-81bd-4be78cfffaf4 · outbound

This paper cites Roy-Chowdhury, Ananda Theertha Suresh, and Samet Oymak.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Roy-Chowdhury, Ananda Theertha Suresh, and Samet Oymak

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.110157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b8e665a6-0b90-4e3c-9e30-8f64e0741180 · outbound

This paper cites Fedpetuning: When fed- erated learning meets the parameter-efficient tuning methods of pre-trained language models.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Fedpetuning: When fed- erated learning meets the parameter-efficient tuning methods of pre-trained language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.102579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2cb89e28-8369-4d6a-85cb-87b2dfa52604 · outbound

This paper cites an unresolved cited work.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:08.094275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ab97b445-4f68-4f86-a305-c3467b3bad30 · outbound

This paper cites Zhao, Ahmed Roushdy Elkordy, Atul Sharma, Yahya H.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Zhao, Ahmed Roushdy Elkordy, Atul Sharma, Yahya H

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.086729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 242b6e45-58c5-4ce8-aa48-e3632bb7b312 · outbound

This paper cites Deep leakage from gradients.

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning Deep leakage from gradients

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:08.078215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Reference resolution

Outbound references

Pith citing papers