Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs | Paper Citation Record

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

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Communication-efficient learning of deep networks from decentralized data

Reference 1

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

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Federated Learning: Strategies for Improving Communication Efficiency

Reference 2

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This paper cites Fedrec++: Lossless federated recommendation with explicit feedback.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Fedrec++: Lossless federated recommendation with explicit feedback

Reference 3

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This paper cites Fedct: Federated collaborative transfer for recommen- dation.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Fedct: Federated collaborative transfer for recommen- dation

Reference 4

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This paper cites Feddg: Federated domain generalization on medical image seg- mentation via episodic learning in continuous frequency space.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Feddg: Federated domain generalization on medical image seg- mentation via episodic learning in continuous frequency space

Reference 5

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This paper cites Feddad: Federated domain adaptation for object detection.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Feddad: Federated domain adaptation for object detection

Reference 6

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This paper cites Visual object de- tection for privacy-preserving federated learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Visual object de- tection for privacy-preserving federated learning

Reference 7

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Observation e6f39494-f3d4-470e-9c8d-84ea81285cab · outbound

This paper cites A Secure and Efficient Federated Learning Framework for NLP.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs A Secure and Efficient Federated Learning Framework for NLP

Reference 8

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

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Federated learning: Challenges, methods, and future directions

Reference 9

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This paper cites Dfedadmm: Dual constraint controlled model inconsistency for de- centralize federated learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Dfedadmm: Dual constraint controlled model inconsistency for de- centralize federated learning

Reference 10

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This paper cites Advances and open problems in federated learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Advances and open problems in federated learning

Reference 11

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This paper cites Dispfl: Towards communication-efficient personalized federated learning via decentralized sparse training.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Dispfl: Towards communication-efficient personalized federated learning via decentralized sparse training

Reference 12

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This paper cites Man- in-the-middle attacks against machine learning classifiers via malicious generative models.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Man- in-the-middle attacks against machine learning classifiers via malicious generative models

Reference 13

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This paper cites A survey on se- curity and privacy of federated learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs A survey on se- curity and privacy of federated learning

Reference 14

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

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs See through gradients: Image batch recovery via gradinversion

Reference 15

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This paper cites One-Shot Federated Learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs One-Shot Federated Learning

Reference 16

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This paper cites Modeldb: a system for machine learning model management.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Modeldb: a system for machine learning model management

Reference 17

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This paper cites Parametric Feature Transfer: One-shot Federated Learning with Foundation Models.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Parametric Feature Transfer: One-shot Federated Learning with Foundation Models

Reference 18

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This paper cites Dense: Data-free one-shot fed- erated learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Dense: Data-free one-shot fed- erated learning

Reference 19

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This paper cites Fine-tuning global model via data-free knowledge dis- tillation for non-iid federated learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Fine-tuning global model via data-free knowledge dis- tillation for non-iid federated learning

Reference 20

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This paper cites Distilled One-Shot Federated Learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Distilled One-Shot Federated Learning

Reference 21

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This paper cites Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

Reference 22

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This paper cites Towards addressing label skews in one-shot federated learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Towards addressing label skews in one-shot federated learning

Reference 23

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This paper cites Available: https://openreview.net/forum? id=rzrqh85f4Sc.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Available: https://openreview.net/forum? id=rzrqh85f4Sc

Reference 24

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This paper cites Data-free one-shot federated learning under very high statistical heterogeneity.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Data-free one-shot federated learning under very high statistical heterogeneity

Reference 25

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This paper cites Exploring one- shot semi-supervised federated learning with pre-trained diffusion models.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Exploring one- shot semi-supervised federated learning with pre-trained diffusion models

Reference 26

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Model in- version attacks that exploit confidence information and basic countermeasures

Reference 27

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Model inversion attacks against collaborative inference

Reference 28

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment

Reference 29

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Data-free knowl- edge distillation via feature exchange and activation region constraint

Reference 30

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Deep classifier mimicry without data access

Reference 31

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This paper cites Learning to retain while acquiring: combating distribution-shift in adver- sarial data-free knowledge distillation.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Learning to retain while acquiring: combating distribution-shift in adver- sarial data-free knowledge distillation

Reference 32

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Dreaming to distill: Data-free knowledge transfer via deepinversion

Reference 33

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 34

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Attention is all you need

Reference 35

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Training data-efficient image trans- formers & distillation through attention

Reference 36

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting

Reference 37

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Inverting visual represen- tations with convolutional networks

Reference 38

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Ensemble distillation for robust model fusion in federated learning

Reference 39

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs All tokens matter: Token labeling for training better vision transformers

Reference 40

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations

Reference 41

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Stability and generaliza- tion

Reference 42

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Train faster, gener- alize better: Stability of stochastic gradient descent

Reference 43

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Unresolved cited work

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs A statistical perspective on distillation

Reference 45

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This paper cites Dreaming to dis- till: Data-free knowledge transfer via deepinversion.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Dreaming to dis- till: Data-free knowledge transfer via deepinversion

Reference 46

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Learning multiple layers of features from tiny images

Reference 47

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Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Deep hashing network for unsupervised do- main adaptation

Reference 48

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This paper cites Matching networks for one shot learning.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Matching networks for one shot learning

Reference 49

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Observation 0084ce2e-df7e-418a-9606-63659663e40b · outbound

This paper cites Imagenet large scale visual recognition challenge.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Imagenet large scale visual recognition challenge

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:51:36.350339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ad240f44-fc4c-4276-ab9f-870ca5016769 · outbound

This paper cites Mohri, A.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Mohri, A

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:51:36.392192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7ea896ba-2c2c-4989-ad9d-56e55cdc6e6a · outbound

This paper cites Co-Boosting [36] uses the current Ensemble to synthesize higher-quality samples in an adversarial manner.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Co-Boosting [36] uses the current Ensemble to synthesize higher-quality samples in an adversarial manner

Reference 52

Resolution: malformed identifier; arxiv_id, observed 2026-05-12T06:01:23.627729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c45861b-0a21-4881-ac0a-74bf7b3ae3d7 · outbound

This paper cites an unresolved cited work.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-05-12T13:51:36.315811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation de047e67-1238-4353-a9fc-87c578d904b7 · outbound

This paper cites The error signal isδ DI =p(X)−y, wherep(X)is the softmax probability.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs The error signal isδ DI =p(X)−y, wherep(X)is the softmax probability

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:51:36.357817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5fd2a6e7-629d-4fab-9792-1ba347d9f7fb · outbound

This paper cites Mechanism 1: Sparsity as Gradient Elimination.For Term 1, we apply a binary maskMsuch that the effective input ˜X=M⊙X, where ˜Xj = 0for noise indicesj∈ I l.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Mechanism 1: Sparsity as Gradient Elimination.For Term 1, we apply a binary maskMsuch that the effective input ˜X=M⊙X, where ˜Xj = 0for noise indicesj∈ I l

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:51:36.339083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 81733f77-15dd-4115-abe1-5e85c8937d10 · outbound

This paper cites an unresolved cited work.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-05-12T13:51:36.380820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d0ff6af3-9a06-4962-96bb-9c8aec4ceb78 · outbound

This paper cites L(noise) DI ∝sup (∥X noise∥ · ∥δhard∥) Under Assumption 2,∥δ hard∥is saturated (large) due to orthogonality.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs L(noise) DI ∝sup (∥X noise∥ · ∥δhard∥) Under Assumption 2,∥δ hard∥is saturated (large) due to orthogonality

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:51:36.433321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 33ad1a7b-199a-4ccc-858e-ecc5f02ab8a0 · outbound

This paper cites Consequently, the backpropagated gradient norm is: ∥ ˜X T AT δhard∥=∥0·A T δhard∥= 0 •Relabeling Term:The input isX noise, but the error signal isδ sof t.

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs Consequently, the backpropagated gradient norm is: ∥ ˜X T AT δhard∥=∥0·A T δhard∥= 0 •Relabeling Term:The input isX noise, but the error signal isδ sof t

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:51:36.324022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cfe4e28d-0568-4069-b3bb-1fb381e26fc3 · outbound

This paper cites According to Lemma 3, the expected norm of the error signal from soft labels is strictly smaller than that from hard labels due to variance reduction:E[∥δ sof t∥]≪E[∥δ hard∥].

Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs According to Lemma 3, the expected norm of the error signal from soft labels is strictly smaller than that from hard labels due to variance reduction:E[∥δ sof t∥]≪E[∥δ hard∥]

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:51:36.418237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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Reference resolution

Outbound references

Pith citing papers