Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data | Paper Citation Record

Observation 422c5dee-27f4-4e1d-9003-0e70a570cd86 · outbound

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

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Communication-efficient learning of deep networks from decentralized data,

Reference 1

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This paper cites Federated learning for privacy- preserving ai,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Federated learning for privacy- preserving ai,

Reference 2

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This paper cites Federated machine learning: Concept and applications,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Federated machine learning: Concept and applications,

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Observation 9e4da73f-9bf0-40c9-8da5-9c4cb997bf7e · outbound

This paper cites Vertical Federated Learning: Concepts, Advances and Challenges.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Vertical Federated Learning: Concepts, Advances and Challenges

Reference 4

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This paper cites Federated learning in mobile edge networks: A comprehensive survey,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Federated learning in mobile edge networks: A comprehensive survey,

Reference 5

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Observation c037080a-fd27-44f7-ab5f-f0a18611a71e · outbound

This paper cites Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Reference 6

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Observation 733d0272-7c7c-40be-8293-2da4b44a3b74 · outbound

This paper cites Fedcvt: Semi-supervised vertical federated learning with cross-view training,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Fedcvt: Semi-supervised vertical federated learning with cross-view training,

Reference 7

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Observation c86e6f2b-9c3d-4ac7-9ba4-89b39406ea34 · outbound

This paper cites Vertical Federated Learning: Challenges, Methodologies and Experiments.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Vertical Federated Learning: Challenges, Methodologies and Experiments

Reference 8

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Observation 24f4df2a-6237-488e-8aad-f978fc1c8f81 · outbound

This paper cites Semi-supervised federated heterogeneous transfer learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Semi-supervised federated heterogeneous transfer learning,

Reference 9

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Observation 4f8e1d83-22db-42a6-8836-c8c805e381b1 · outbound

This paper cites A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning

Reference 10

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This paper cites Self-supervised Cross-silo Federated Neural Architecture Search.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Self-supervised Cross-silo Federated Neural Architecture Search

Reference 11

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Observation 1babcd15-d9ed-487e-b75e-dbef93618381 · outbound

This paper cites Self-supervised vertical feder- ated learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Self-supervised vertical feder- ated learning,

Reference 12

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Observation 2f1d9969-731a-4cf6-8ec4-f5b102697006 · outbound

This paper cites Vertical semi- federated learning for efficient online advertising,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Vertical semi- federated learning for efficient online advertising,

Reference 13

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Observation 2307e8f4-8691-4774-9302-748fcb666a7e · outbound

This paper cites Multi-view federated learning with data collaboration,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Multi-view federated learning with data collaboration,

Reference 14

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Observation 00ce1846-c076-413d-a302-aa5147b19065 · outbound

This paper cites Vertical federated learning-based feature selection with non- overlapping sample utilization,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Vertical federated learning-based feature selection with non- overlapping sample utilization,

Reference 15

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Observation 8770ab97-bf49-428b-9499-f70cb9b2e92f · outbound

This paper cites Practical vertical federated learning with unsupervised representation learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Practical vertical federated learning with unsupervised representation learning,

Reference 16

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Observation 68cefc2f-12e2-4976-9f86-6dc16350d738 · outbound

This paper cites A review of the oversampling techniques in class imbalance problem,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data A review of the oversampling techniques in class imbalance problem,

Reference 17

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Observation 84a1e2d4-2d4e-48d4-b02a-57acb5f03ce5 · outbound

This paper cites A review on imbalanced data handling using undersampling and oversampling technique,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data A review on imbalanced data handling using undersampling and oversampling technique,

Reference 18

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Observation a3ee2bd1-dc5f-4cb9-a0c2-74686f8a28dd · outbound

This paper cites Overcoming Noisy and Irrelevant Data in Federated Learning.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Overcoming Noisy and Irrelevant Data in Federated Learning

Reference 19

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Observation 294525c7-8d89-4c6a-b1f8-b641b253d10d · outbound

This paper cites Three Approaches for Personalization with Applications to Federated Learning.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Three Approaches for Personalization with Applications to Federated Learning

Reference 20

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Observation 95d3a178-ae30-414c-bb40-c21edd52b231 · outbound

This paper cites Attribute-based classifi- cation for zero-shot visual object categorization,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Attribute-based classifi- cation for zero-shot visual object categorization,

Reference 21

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Observation ce3ccc6b-68e3-4686-9d74-5d66f16b402a · outbound

This paper cites Zero-data learning of new tasks.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Zero-data learning of new tasks

Reference 22

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Observation f2c68f2c-a0b6-4ee4-88a6-24c89e97d36a · outbound

This paper cites Learning hypergraph-regularized attribute predictors,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Learning hypergraph-regularized attribute predictors,

Reference 23

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Observation 56b87a91-c277-4faa-b49d-8373b01ee3eb · outbound

This paper cites Learning multimodal latent attributes,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Learning multimodal latent attributes,

Reference 24

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Observation 6626cc98-1d2f-4439-93b2-5d3e3864f8a7 · outbound

This paper cites Zero-shot recognition with unreliable attributes,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Zero-shot recognition with unreliable attributes,

Reference 25

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This paper cites Attribute-based classifi- cation for zero-shot visual object categorization,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Attribute-based classifi- cation for zero-shot visual object categorization,

Reference 26

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This paper cites Generative zero-shot learning via low- rank embedded semantic dictionary,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Generative zero-shot learning via low- rank embedded semantic dictionary,

Reference 27

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This paper cites Zero-shot learning via latent space encoding,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Zero-shot learning via latent space encoding,

Reference 28

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This paper cites Transduc- tive zero-shot learning with a self-training dictionary approach,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Transduc- tive zero-shot learning with a self-training dictionary approach,

Reference 29

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This paper cites Feature Generating Networks for Zero-Shot Learning.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Feature Generating Networks for Zero-Shot Learning

Reference 30

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This paper cites General- ized zero- and few-shot learning via aligned variational autoencoders,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data General- ized zero- and few-shot learning via aligned variational autoencoders,

Reference 31

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This paper cites Fedproto: Federated prototype learning across heterogeneous clients,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Fedproto: Federated prototype learning across heterogeneous clients,

Reference 32

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This paper cites Fedfa: Federated learning with feature anchors to align features and classifiers for heterogeneous data,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Fedfa: Federated learning with feature anchors to align features and classifiers for heterogeneous data,

Reference 33

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Observation e0e4ccfc-75a6-4a18-9d6d-eb3a86e5ef0d · outbound

This paper cites Personalized Federated Learning with Feature Alignment and Classifier Collaboration.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Personalized Federated Learning with Feature Alignment and Classifier Collaboration

Reference 34

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Observation 7512860c-5ca1-4e9e-8683-e19d47f936d4 · outbound

This paper cites Tackling data heterogeneity in federated learning with class prototypes,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Tackling data heterogeneity in federated learning with class prototypes,

Reference 35

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Observation 7d2bf1fb-8626-4dfa-9bb5-7a83744a9013 · outbound

This paper cites Fedproc: Prototypical contrastive federated learning on non-iid data,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Fedproc: Prototypical contrastive federated learning on non-iid data,

Reference 36

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Observation 56672f37-b571-483f-ae45-e1bf670bd333 · outbound

This paper cites Contrastive-enhanced domain generalization with federated learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Contrastive-enhanced domain generalization with federated learning,

Reference 37

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Observation 4e20427e-9249-4e11-bbc8-20f7168b1de3 · outbound

This paper cites Vertical federated knowledge trans- fer via representation distillation for healthcare collaboration networks,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Vertical federated knowledge trans- fer via representation distillation for healthcare collaboration networks,

Reference 38

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This paper cites Improving availability of vertical federated learning: Relaxing inference on non-overlapping data,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Improving availability of vertical federated learning: Relaxing inference on non-overlapping data,

Reference 39

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Observation be3f8c53-9628-4b4c-ba89-56cac966b8e4 · outbound

This paper cites Entity Resolution and Federated Learning get a Federated Resolution.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Entity Resolution and Federated Learning get a Federated Resolution

Reference 40

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Observation 047fdd2e-f266-4baf-b8db-394023011103 · outbound

This paper cites Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

Reference 41

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This paper cites Opti- mal transport based one-shot federated learning for artificial intelligence of things,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Opti- mal transport based one-shot federated learning for artificial intelligence of things,

Reference 42

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Observation 5905b1b4-ec77-40b7-8c3d-ebd24334b6fc · outbound

This paper cites Global and local prompts coop- eration via optimal transport for federated learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Global and local prompts coop- eration via optimal transport for federated learning,

Reference 43

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Observation 69a7c1d5-49cb-4d73-8500-e927d8f9bb4c · outbound

This paper cites Spectr: Fast speculative decoding via optimal transport,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Spectr: Fast speculative decoding via optimal transport,

Reference 44

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This paper cites Bayes’ theorem,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Bayes’ theorem,

Reference 45

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Observation 9125c0be-3310-4f42-854e-c7b0f917bbbc · outbound

This paper cites Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation

Reference 46

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Observation da953bc6-5be2-4a06-a35a-50eab2e385b0 · outbound

This paper cites Entropy Minimization vs. Diversity Maximization for Domain Adaptation.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Entropy Minimization vs. Diversity Maximization for Domain Adaptation

Reference 47

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Observation 454f8b5c-fc58-415e-8d99-abd022a4fd1e · outbound

This paper cites von liebig’s law of the minimum and plankton ecology (1899–1991),.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data von liebig’s law of the minimum and plankton ecology (1899–1991),

Reference 48

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Observation 0926fba9-0aa8-40d0-834a-88250c07d6d7 · outbound

This paper cites Enhancing supervised learning with unlabeled data,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Enhancing supervised learning with unlabeled data,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.786160Z

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Observation f89f11f6-6188-44cc-8fe3-02ad37a6553e · outbound

This paper cites Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.776028Z

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Observation e34ba576-d6b6-4477-86bb-a9b1ffb64dc8 · outbound

This paper cites A unified solution for privacy and communication efficiency in vertical federated learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data A unified solution for privacy and communication efficiency in vertical federated learning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.765928Z

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Observation 89519635-eeb9-489e-96fa-97594ca69e8f · outbound

This paper cites Flexible vertical federated learning with heterogeneous parties,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Flexible vertical federated learning with heterogeneous parties,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.756524Z

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 18b257ab-f8e8-4b36-bc2d-b945c221a355 · outbound

This paper cites Twenty years of mixture of experts,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Twenty years of mixture of experts,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.745262Z

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 0abe2e64-7f0d-4ad6-b798-c347f1f2fa93 · outbound

This paper cites Less-vfl: Communication-efficient feature selection for vertical federated learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Less-vfl: Communication-efficient feature selection for vertical federated learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.737160Z

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 21f16f50-06a8-4467-90c6-a9c56ab4dfe8 · outbound

This paper cites Label inference attacks against vertical federated learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Label inference attacks against vertical federated learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.727589Z

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 24186fcb-dd01-4492-8785-aadc5fa4f3df · outbound

This paper cites Practical feature inference attack in vertical federated learning during prediction in artificial internet of things,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Practical feature inference attack in vertical federated learning during prediction in artificial internet of things,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.718738Z

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 e16d27f0-9bc4-4e9a-92e1-f7038bd7981f · outbound

This paper cites Approximation Methods for Bilevel Programming.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Approximation Methods for Bilevel Programming

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:59.105558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea56169b-28b7-46da-8e9a-111ef43a392b · outbound

This paper cites Convergence of meta- learning with task-specific adaptation over partial parameters,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Convergence of meta- learning with task-specific adaptation over partial parameters,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.710210Z

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 4368138e-24d9-4c55-aac1-9f19d201ec17 · outbound

This paper cites Closing the convergence gap of sgd without replacement,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Closing the convergence gap of sgd without replacement,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.700527Z

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 45b31715-1114-41d4-a1fb-3d02a8ad6b9e · outbound

This paper cites Bilevel optimization: Convergence analysis and enhanced design,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Bilevel optimization: Convergence analysis and enhanced design,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.691274Z

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 d3c34287-9655-4ff1-bed3-aa87bbe1b75b · outbound

This paper cites Fastslowmo: Federated learning with combined worker and aggregator momenta,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Fastslowmo: Federated learning with combined worker and aggregator momenta,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.681377Z

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 c46b0ed9-77a0-4165-8951-8719be90e846 · outbound

This paper cites General data protection regulation,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data General data protection regulation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.670160Z

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 d42d6f06-4732-4492-96dc-d6284f8f3486 · outbound

This paper cites Inverting gradients-how easy is it to break privacy in federated learning?.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Inverting gradients-how easy is it to break privacy in federated learning?

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.660286Z

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 29fe5cda-d171-4cb6-a0ad-849f3809af1b · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data 3d shapenets: A deep representation for volumetric shapes,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.649497Z

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 9bb2134f-58a5-4e83-a376-a6c2e9574202 · outbound

This paper cites Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.640292Z

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 699b0aee-af7f-4cd0-9495-54dae02250df · outbound

This paper cites Default of Credit Card Clients,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Default of Credit Card Clients,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:59.138329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8394728a-7454-41c4-8fee-6755f68f958b · outbound

This paper cites Becker and R.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Becker and R

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T11:45:59.141691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c161396-f952-47ac-9aba-2105dcc9c2e7 · outbound

This paper cites A method for stochastic optimization,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data A method for stochastic optimization,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.631350Z

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 7e3a88c6-a518-4b11-b794-4c1adc3d9bd3 · outbound

This paper cites An experimental study of class imbalance in federated learning,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data An experimental study of class imbalance in federated learning,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.621369Z

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 d94b314f-711a-476f-966b-50cca4dcdd0b · outbound

This paper cites Semantic cosine similar- ity,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Semantic cosine similar- ity,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.610542Z

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 610503d5-692c-4398-ab78-273ad32134cf · outbound

This paper cites Learning with a wasserstein loss,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Learning with a wasserstein loss,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.599979Z

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 c9adcccb-5f1a-44b6-8ee9-2723b46912da · outbound

This paper cites Semi-supervised cross-silo advertising with partial knowledge transfer,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Semi-supervised cross-silo advertising with partial knowledge transfer,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.589168Z

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 934502b1-c3b4-4bd7-9aee-b45eaf202f5b · outbound

This paper cites Differential privacy,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Differential privacy,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.579434Z

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 f1064a29-d076-4c9a-9f69-af57d51ca604 · outbound

This paper cites Semi-supervised learning by entropy minimization,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Semi-supervised learning by entropy minimization,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.569428Z

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 ba42142a-c51f-4479-8dd7-e56532c18544 · outbound

This paper cites Semi- supervised domain adaptation via minimax entropy,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Semi- supervised domain adaptation via minimax entropy,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.558884Z

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 4c2ca93e-9288-4da4-a3b8-569e690f5a97 · outbound

This paper cites Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.549028Z

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 3b2d5537-c8f3-4b35-ab71-786a90911e12 · outbound

This paper cites Universal domain adaptation through self supervision,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Universal domain adaptation through self supervision,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.538800Z

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 76fb7c29-a8c1-4451-8a82-74a5eec436a9 · outbound

This paper cites Robust optimal transport with applications in generative modeling and domain adaptation,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Robust optimal transport with applications in generative modeling and domain adaptation,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.528156Z

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 db0b67c7-ed31-4617-8d50-3336323db867 · outbound

This paper cites an unresolved cited work.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:46:00.518146Z

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 a39fc52f-6778-40d5-b619-8e0870c5e110 · outbound

This paper cites Cross-silo federated neural architecture search for heterogeneous and cooperative systems,.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Cross-silo federated neural architecture search for heterogeneous and cooperative systems,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.507715Z

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 be6ed47d-da7d-44d8-a1d6-c188ea5a20e3 · outbound

This paper cites Likelihood.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Likelihood

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.495859Z

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 3665db38-f513-4277-bf51-242c649f5a7c · outbound

This paper cites The target label of each data point, Y m,n, can not be directly obtained by observation.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data The target label of each data point, Y m,n, can not be directly obtained by observation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.484764Z

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 07d2ecd9-54e2-474a-b35d-29f19a23a14a · outbound

This paper cites To show the smooth property of F (Θ), we first introduce the following lemma which is proposed in [57].

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data To show the smooth property of F (Θ), we first introduce the following lemma which is proposed in [57]

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.473807Z

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 200e3c51-f475-40b8-9af4-8ea09b7d55fb · outbound

This paper cites Other nota- tions involved Bj or Bj such as ∇Θ∇E llocal(Θ0 t , E j−1 t ; Bj−1) have similar meanings.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Other nota- tions involved Bj or Bj such as ∇Θ∇E llocal(Θ0 t , E j−1 t ; Bj−1) have similar meanings

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.463598Z

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 08aa53b7-1b6f-4e91-81c3-82e27c188bd2 · outbound

This paper cites (77) This is a more general result of Theorem 1.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data (77) This is a more general result of Theorem 1

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.452610Z

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 57d2adfc-6e09-42a1-956a-64e9dd591437 · outbound

This paper cites an unresolved cited work.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:46:00.438702Z

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 caa86502-f348-4c7a-8469-d7915e487488 · outbound

This paper cites nc represents the number of samples in class z.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data nc represents the number of samples in class z

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.388302Z

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 f34ad314-74a7-42aa-8ac2-6a49df8c75bc · outbound

This paper cites Available: https://proceedings.neurips.cc/paper files/ paper/2014/file/1f1baa5b8edac74eb4eaa329f14a0361-Paper.pdf.

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data Available: https://proceedings.neurips.cc/paper files/ paper/2014/file/1f1baa5b8edac74eb4eaa329f14a0361-Paper.pdf

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:46:00.964883Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers