Paper Citation Record · LEDGER

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

As of 13 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2412.00334.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.00334 v2

Coverage vector

measured 43 of 43 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 3496512c-789d-4165-a62c-aa15fad69fc8 · outbound

This paper cites Fe- drolex: Model-heterogeneous federated learning with rolling sub-model extraction.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Fe- drolex: Model-heterogeneous federated learning with rolling sub-model extraction

Reference 1

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Observation 04aeb87c-cfb1-4033-9fb9-2bf79d9f3147 · outbound

This paper cites Dearkd: data-efficient early knowledge distillation for vision transformers.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Dearkd: data-efficient early knowledge distillation for vision transformers

Reference 2

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Observation da6c5fb3-7168-4b84-9693-a222c524ddd6 · outbound

This paper cites Up-detr: Unsupervised pre-training for object detection with transformers.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Up-detr: Unsupervised pre-training for object detection with transformers

Reference 3

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Observation 75f68a04-33c7-4a49-88cf-cd0c6ad78177 · outbound

This paper cites Heterofl: Com- putation and communication efficient federated learning for heterogeneous clients.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Heterofl: Com- putation and communication efficient federated learning for heterogeneous clients

Reference 4

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Observation d22f5596-c3cd-44bf-83a6-195ef139ac16 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

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Observation dbe1cd4a-7d84-4abd-acb8-e51c24a24cfb · outbound

This paper cites Differential privacy: A survey of results.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Differential privacy: A survey of results

Reference 6

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Observation b68b747b-ac56-4ab4-8e39-33e318d70de4 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 7

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Observation 97526822-f426-4640-9fad-2afbcacd780f · outbound

This paper cites Group knowledge transfer: Federated learning of large cnns at the edge.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Group knowledge transfer: Federated learning of large cnns at the edge

Reference 8

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Observation 2ba756e4-825c-49c7-a300-2c3e8bbfb3ec · outbound

This paper cites Deep residual learning for image recognition.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Deep residual learning for image recognition

Reference 9

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Observation 4027eeb1-e0be-4fc5-aff9-96f638edef91 · outbound

This paper cites Masked autoencoders are scalable vision learners.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Masked autoencoders are scalable vision learners

Reference 10

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Observation 3bebbe83-5644-4c69-8473-f06a060ecc1d · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

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Observation 76b026c1-7e14-4f37-92f2-da15eb93259d · outbound

This paper cites Adaptivefl: Adaptive heterogeneous federated learning for resource-constrained aiot systems.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Adaptivefl: Adaptive heterogeneous federated learning for resource-constrained aiot systems

Reference 12

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Observation 265ecc59-54be-4553-8d53-224a023ae9d5 · outbound

This paper cites Vi- sual prompt tuning.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Vi- sual prompt tuning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:10.907506Z

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Observation 843dd483-8339-42b4-a814-843aaef253fb · outbound

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

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Learning multiple layers of features from tiny images

Reference 14

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Observation c0e1f6d5-56b3-4277-bf3a-2fb14327df94 · outbound

This paper cites Unif2ace: Fine- grained face understanding and generation with unified mul- timodal models.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Unif2ace: Fine- grained face understanding and generation with unified mul- timodal models

Reference 15

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Observation e269e899-7d80-4d8e-9470-95e9a3338836 · outbound

This paper cites Self- supervised geometric features discovery via interpretable at- tention for vehicle re-identification and beyond.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Self- supervised geometric features discovery via interpretable at- tention for vehicle re-identification and beyond

Reference 16

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Observation 29ec8c05-b6b1-4015-a9f9-30d94eac3d62 · outbound

This paper cites Exploiting multi-view part-wise correlation via an ef- ficient transformer for vehicle re-identification.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Exploiting multi-view part-wise correlation via an ef- ficient transformer for vehicle re-identification

Reference 17

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This paper cites Dr-fer: Discrimina- tive and robust representation learning for facial expression recognition.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Dr-fer: Discrimina- tive and robust representation learning for facial expression recognition

Reference 18

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EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Stprivacy: Spatio-temporal privacy-preserving action recognition

Reference 19

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Observation 575c03d9-16df-40d7-82d5-6b53d03d64b9 · outbound

This paper cites Instant3d: instant text- to-3d generation.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Instant3d: instant text- to-3d generation

Reference 20

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Observation 334e1d88-243d-4b06-a748-bbda583f9d5f · outbound

This paper cites Finch: Enhancing federated learning with hierarchical neural architecture search.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Finch: Enhancing federated learning with hierarchical neural architecture search

Reference 21

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Observation 4b752083-205c-4dfe-bb57-1c1fd2c76331 · outbound

This paper cites Eventgpt: Event stream understanding with multimodal large language models.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Eventgpt: Event stream understanding with multimodal large language models

Reference 22

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Observation 271dacb8-4779-4a57-a397-3318359f1708 · outbound

This paper cites RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining

Reference 23

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

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Communication- efficient learning of deep networks from decentralized data

Reference 24

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Observation 3af8a569-da83-4937-9c32-71cf9a4cd519 · outbound

This paper cites ImageNet-21K Pretraining for the Masses.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients ImageNet-21K Pretraining for the Masses

Reference 25

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Observation e1340f12-16b1-4821-8222-b2a9532a74ff · outbound

This paper cites How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Reference 26

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Observation 3eb7e427-8597-4892-b092-15bcd940e2bb · outbound

This paper cites Fedra: A ran- dom allocation strategy for federated tuning to unleash the power of heterogeneous clients.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Fedra: A ran- dom allocation strategy for federated tuning to unleash the power of heterogeneous clients

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:10.791544Z

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Observation d117651f-ee31-4402-a793-0dc9ff60f872 · outbound

This paper cites Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning

Reference 28

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Observation 0f8176f7-dbdf-4355-b643-62b832a48834 · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Training data-efficient image transformers & distillation through at- tention

Reference 29

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Observation 86245336-6e7d-4897-9c30-8b02134aa62c · outbound

This paper cites Fedekt: Ensem- ble knowledge transfer for model-heterogeneous federated learning.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Fedekt: Ensem- ble knowledge transfer for model-heterogeneous federated learning

Reference 30

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Observation 67ff46c8-fa56-459a-9a84-842573238b85 · outbound

This paper cites Revealing the dark secrets of masked im- age modeling.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Revealing the dark secrets of masked im- age modeling

Reference 31

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This paper cites Fedbfpt: An efficient federated learning framework for bert further pre-training.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Fedbfpt: An efficient federated learning framework for bert further pre-training

Reference 32

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EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Unresolved cited work

Reference 33

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This paper cites Bag-of-visual-words and spa- tial extensions for land-use classification.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Bag-of-visual-words and spa- tial extensions for land-use classification

Reference 34

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Observation a4552902-ba8b-498d-afd9-f68eac9415fb · outbound

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

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients See through gradients: Image batch recovery via gradinversion

Reference 35

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This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 36

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EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Fedpetuning: When fed- erated learning meets the parameter-efficient tuning methods of pre-trained language models

Reference 37

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

This paper cites iDLG: Improved Deep Leakage from Gradients.

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

Reference 38

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This paper cites FaVChat: Hierarchical Prompt-Query Guided Facial Video Understanding with Data-Efficient GRPO.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients FaVChat: Hierarchical Prompt-Query Guided Facial Video Understanding with Data-Efficient GRPO

Reference 39

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EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

Reference 40

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EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Deep leakage from gradients

Reference 41

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This paper cites Vistorybench: Comprehensive benchmark suite for story visualization.

EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Vistorybench: Comprehensive benchmark suite for story visualization

Reference 42

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EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients Unresolved cited work

Reference 9977

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Pith citing papers

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