GeFL: Model-Agnostic Federated Learning with Generative Models | Paper Citation Record

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This paper cites Kang and S.

GeFL: Model-Agnostic Federated Learning with Generative Models Kang and S

Reference 1

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Observation d8ae6585-6222-453d-bcce-a10838feb99f · outbound

This paper cites McMahan, E.

GeFL: Model-Agnostic Federated Learning with Generative Models McMahan, E

Reference 2

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Observation 8e4c8eb8-4eef-4f29-a0b6-a8653db0f667 · outbound

This paper cites NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients.

GeFL: Model-Agnostic Federated Learning with Generative Models NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients

Reference 3

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This paper cites Afonin and S.

GeFL: Model-Agnostic Federated Learning with Generative Models Afonin and S

Reference 4

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Observation 2e919de0-eeb5-4ad6-b175-a412a5152d1c · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

GeFL: Model-Agnostic Federated Learning with Generative Models Gemini: A Family of Highly Capable Multimodal Models

Reference 5

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This paper cites Brown, B.

GeFL: Model-Agnostic Federated Learning with Generative Models Brown, B

Reference 6

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Observation 92f82b58-cb24-46e8-a432-ba8f2b8cce63 · outbound

This paper cites Machine Learning Model Sizes and the Parameter Gap.

GeFL: Model-Agnostic Federated Learning with Generative Models Machine Learning Model Sizes and the Parameter Gap

Reference 7

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Observation 45d701c6-6e5d-42b8-b263-c5c10f7af736 · outbound

This paper cites Pfeiffer, M.

GeFL: Model-Agnostic Federated Learning with Generative Models Pfeiffer, M

Reference 8

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GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 9

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This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 10

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Observation 18d5e110-706c-4c6d-b0fa-e249824bbd26 · outbound

This paper cites Horv\' a th, S.

GeFL: Model-Agnostic Federated Learning with Generative Models Horv\' a th, S

Reference 11

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GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 12

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This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 13

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Observation e9d1d206-ba2b-4223-a704-b4581df383fa · outbound

This paper cites Huang, M.

GeFL: Model-Agnostic Federated Learning with Generative Models Huang, M

Reference 14

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This paper cites FedMD: Heterogenous Federated Learning via Model Distillation.

GeFL: Model-Agnostic Federated Learning with Generative Models FedMD: Heterogenous Federated Learning via Model Distillation

Reference 15

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This paper cites FedGH: Heterogeneous Federated Learning with Generalized Global Header.

GeFL: Model-Agnostic Federated Learning with Generative Models FedGH: Heterogeneous Federated Learning with Generalized Global Header

Reference 16

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GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 17

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Observation 02661383-fe6c-4f20-bb0a-d76bd405f799 · outbound

This paper cites Auto-Encoding Variational Bayes.

GeFL: Model-Agnostic Federated Learning with Generative Models Auto-Encoding Variational Bayes

Reference 18

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Observation c08b7e67-0eca-4818-abc9-dddc8877ce1c · outbound

This paper cites Sohl-Dickstein, E.

GeFL: Model-Agnostic Federated Learning with Generative Models Sohl-Dickstein, E

Reference 19

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Observation 691014a2-2777-43f9-bb25-6cbee8146e54 · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 20

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Observation 0923ec32-0edb-41b5-8881-3c529fc87684 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

GeFL: Model-Agnostic Federated Learning with Generative Models Distilling the Knowledge in a Neural Network

Reference 21

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Observation 7d3881c8-314d-46d2-8b65-41004354985c · outbound

This paper cites Federated Knowledge Distillation.

GeFL: Model-Agnostic Federated Learning with Generative Models Federated Knowledge Distillation

Reference 22

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GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 23

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This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 24

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This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 25

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Observation ce485c7e-362f-46ab-84c7-0e76f8d87f89 · outbound

This paper cites Think Locally, Act Globally: Federated Learning with Local and Global Representations.

GeFL: Model-Agnostic Federated Learning with Generative Models Think Locally, Act Globally: Federated Learning with Local and Global Representations

Reference 26

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Observation 8a7633fe-b8dc-4c68-aaa8-bcf96e895d7e · outbound

This paper cites Federated Mutual Learning.

GeFL: Model-Agnostic Federated Learning with Generative Models Federated Mutual Learning

Reference 27

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Observation acd711e3-95cd-469e-b5df-c516a14b8080 · outbound

This paper cites Towards Personalized Federated Learning via Heterogeneous Model Reassembly.

GeFL: Model-Agnostic Federated Learning with Generative Models Towards Personalized Federated Learning via Heterogeneous Model Reassembly

Reference 28

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Observation 7d6bf53a-25fb-4005-9827-77159f4dae6a · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 29

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Observation 92fd38cf-6914-4a8d-bd81-4da74e975c63 · outbound

This paper cites Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data.

GeFL: Model-Agnostic Federated Learning with Generative Models Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

Reference 30

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Observation f50c3980-3bb4-4687-b064-d185b9f7c57a · outbound

This paper cites FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning.

GeFL: Model-Agnostic Federated Learning with Generative Models FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning

Reference 31

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Observation b389b768-c0d6-414a-98c1-67c574bc5ab0 · outbound

This paper cites FedGAN: Federated Generative Adversarial Networks for Distributed Data.

GeFL: Model-Agnostic Federated Learning with Generative Models FedGAN: Federated Generative Adversarial Networks for Distributed Data

Reference 32

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GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 33

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Observation d1969529-81b6-42c1-abe3-d053eafc997c · outbound

This paper cites Zhang, L.

GeFL: Model-Agnostic Federated Learning with Generative Models Zhang, L

Reference 34

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This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 35

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Observation 4cb2a77d-9e04-4294-96c4-95357408c052 · outbound

This paper cites Tan and Q.

GeFL: Model-Agnostic Federated Learning with Generative Models Tan and Q

Reference 36

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Observation 679c6980-3696-4abd-9e5f-d48282f270d4 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

GeFL: Model-Agnostic Federated Learning with Generative Models MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 37

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Observation 5453b0b9-ee8f-41ae-a18d-c98e68e502f9 · outbound

This paper cites LeCun, L.

GeFL: Model-Agnostic Federated Learning with Generative Models LeCun, L

Reference 38

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Observation 95703753-b523-47f0-93a7-790289a86c2d · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

GeFL: Model-Agnostic Federated Learning with Generative Models Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 39

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Observation a4d05d04-0ddc-4a9d-8999-4c518c36fac0 · outbound

This paper cites Krizhevsky et al., ``Learning multiple layers of features from tiny images,'' Master's thesis, Department of Computer Science, University of Toronto, 2009.

GeFL: Model-Agnostic Federated Learning with Generative Models Krizhevsky et al., ``Learning multiple layers of features from tiny images,'' Master's thesis, Department of Computer Science, University of Toronto, 2009

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:43.124862Z

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Observation 8cab46ea-9369-441b-ae92-20feedc10a76 · outbound

This paper cites Zhang, Y.

GeFL: Model-Agnostic Federated Learning with Generative Models Zhang, Y

Reference 41

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This paper cites Radford, L.

GeFL: Model-Agnostic Federated Learning with Generative Models Radford, L

Reference 42

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Observation 081fd39f-43a8-4a57-9bfa-567311fa1e04 · outbound

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GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 43

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This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 44

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This paper cites Ho and T.

GeFL: Model-Agnostic Federated Learning with Generative Models Ho and T

Reference 45

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This paper cites Heusel, H.

GeFL: Model-Agnostic Federated Learning with Generative Models Heusel, H

Reference 46

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Observation af9494ac-2503-4886-a904-78b33ce4b561 · outbound

This paper cites Ravuri and O.

GeFL: Model-Agnostic Federated Learning with Generative Models Ravuri and O

Reference 47

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This paper cites Zhang, M.

GeFL: Model-Agnostic Federated Learning with Generative Models Zhang, M

Reference 48

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GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 49

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Observation 5b6d532a-8a54-4d19-bd88-64535f659d2f · outbound

This paper cites Hendrycks, N.

GeFL: Model-Agnostic Federated Learning with Generative Models Hendrycks, N

Reference 50

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Observation 86cf665d-237b-45c6-8199-a1e52f378ccf · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

GeFL: Model-Agnostic Federated Learning with Generative Models AutoAugment: Learning Augmentation Policies from Data

Reference 51

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Observation cc13b47d-dd95-483b-8808-ed39b9b59454 · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:45:43.010295Z

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This paper cites Geiping, H.

GeFL: Model-Agnostic Federated Learning with Generative Models Geiping, H

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.998999Z

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Observation 0a6ae15c-9049-4d72-b839-378eb19e49cf · outbound

This paper cites van den Burg and C.

GeFL: Model-Agnostic Federated Learning with Generative Models van den Burg and C

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.988352Z

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Observation 609e1063-2529-4d0b-a3ec-94cbb5aa11bd · outbound

This paper cites Somepalli, V.

GeFL: Model-Agnostic Federated Learning with Generative Models Somepalli, V

Reference 55

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Observation c1cf82a2-3179-43f1-b9b5-cb50013d53d9 · outbound

This paper cites Webster, J.

GeFL: Model-Agnostic Federated Learning with Generative Models Webster, J

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.963178Z

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This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 57

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Observation 03528e38-5e11-466d-b7d4-01f3f9604b4a · outbound

This paper cites Hilprecht, M.

GeFL: Model-Agnostic Federated Learning with Generative Models Hilprecht, M

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.944146Z

Source-reported events for the cited work

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Observation 7ef3afbc-407e-4f1d-a513-e5961b6b8765 · outbound

This paper cites Abadi, A.

GeFL: Model-Agnostic Federated Learning with Generative Models Abadi, A

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.934659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 80c13d3a-e023-45b3-a5c0-c0b6137821bd · outbound

This paper cites Differentially Private Generative Adversarial Network.

GeFL: Model-Agnostic Federated Learning with Generative Models Differentially Private Generative Adversarial Network

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T04:45:42.421806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb114dc4-9bd9-4a54-8d3e-875d079f579e · outbound

This paper cites Zhang, P.

GeFL: Model-Agnostic Federated Learning with Generative Models Zhang, P

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.924788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4ea3f353-d9d8-4cd8-9bbb-a7abc0cdb642 · outbound

This paper cites Federated Learning with Non-IID Data.

GeFL: Model-Agnostic Federated Learning with Generative Models Federated Learning with Non-IID Data

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T04:45:42.428903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84baa535-16a9-4855-92cc-19968c0202ca · outbound

This paper cites Mahendran and A.

GeFL: Model-Agnostic Federated Learning with Generative Models Mahendran and A

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.914842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5eecbba0-eba0-4591-a796-14a06495535f · outbound

This paper cites Dosovitskiy and T.

GeFL: Model-Agnostic Federated Learning with Generative Models Dosovitskiy and T

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.904144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 812876cc-390e-49ef-b65e-7f547df6eaa2 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

GeFL: Model-Agnostic Federated Learning with Generative Models Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T04:45:42.439538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08f20151-b697-4aa2-89a3-a781e6ccb5c9 · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:45:42.893936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e7ecc678-ed37-456a-9cb1-8c62179b18c6 · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:45:42.883824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8aaebb51-5747-46b3-8415-34dde65c0245 · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:45:42.873550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b51451f4-2137-45e5-a195-4d1395ffcb1f · outbound

This paper cites Netzer, T.

GeFL: Model-Agnostic Federated Learning with Generative Models Netzer, T

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.864133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8fdfd250-9fc0-44d2-a08a-a3790d57ac57 · outbound

This paper cites Krizhevsky, V.

GeFL: Model-Agnostic Federated Learning with Generative Models Krizhevsky, V

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.854363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ac2a234b-9899-4ae0-98da-330b0290f81e · outbound

This paper cites Scaling Laws of Synthetic Images for Model Training ... for Now.

GeFL: Model-Agnostic Federated Learning with Generative Models Scaling Laws of Synthetic Images for Model Training ... for Now

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T04:45:42.461435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1073667a-9b63-49bd-83cb-18cd962bcf83 · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:45:42.844296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d8d32c86-6e22-48de-ad3c-37d705cbdcf0 · outbound

This paper cites Azizi, S.

GeFL: Model-Agnostic Federated Learning with Generative Models Azizi, S

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.834325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 401df375-5b13-4073-8666-142cf8c579a2 · outbound

This paper cites Shmelkov, C.

GeFL: Model-Agnostic Federated Learning with Generative Models Shmelkov, C

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.824283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fb300a0a-95ca-4bfb-abb7-64df9d06ad95 · outbound

This paper cites Yamaguchi, D.

GeFL: Model-Agnostic Federated Learning with Generative Models Yamaguchi, D

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.814436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7f8172a9-9c85-4438-bbd5-b29d46b290c6 · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:45:42.803885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1f773148-fdd4-4da1-b8dc-688cf950cbd5 · outbound

This paper cites an unresolved cited work.

GeFL: Model-Agnostic Federated Learning with Generative Models Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:45:42.791791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 48c1788b-09ee-4e73-9450-09cc4e1d1c07 · outbound

This paper cites Ronneberger, P.

GeFL: Model-Agnostic Federated Learning with Generative Models Ronneberger, P

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.780437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c08b7425-321a-4061-98d6-a30a77e544cf · outbound

This paper cites Pearce, H.

GeFL: Model-Agnostic Federated Learning with Generative Models Pearce, H

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.769985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 70c6fb58-a989-4b3b-8fd8-2941e029bfd5 · outbound

This paper cites Ioffe and C.

GeFL: Model-Agnostic Federated Learning with Generative Models Ioffe and C

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.758720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 65fd326b-bbb8-4390-bda6-de71cffdd534 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

GeFL: Model-Agnostic Federated Learning with Generative Models Deep Learning using Rectified Linear Units (ReLU)

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T04:45:42.493418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61cf52e4-88b2-4f21-9f73-181a07ecb21c · outbound

This paper cites Meehan, K.

GeFL: Model-Agnostic Federated Learning with Generative Models Meehan, K

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:45:42.747548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers