GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning | Paper Citation Record

Observation 2ceb9b11-66bb-4d99-99c6-6d21950b2096 · outbound

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

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 1

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Observation 6bbc1a63-0c61-4054-b3b1-e0dd95f71b71 · outbound

This paper cites Calfat: Calibrated federated ad- versarial training with label skewness,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Calfat: Calibrated federated ad- versarial training with label skewness,

Reference 2

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Observation 9d9821fc-c615-4772-b6b7-e038314dae15 · outbound

This paper cites Cross-silo feature space alignment for federated learning on clients with imbalanced data,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Cross-silo feature space alignment for federated learning on clients with imbalanced data,

Reference 3

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Observation b5104dd3-e2c4-4587-bb37-a781af726dd0 · outbound

This paper cites Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets,

Reference 4

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Observation bf87a791-d9d8-4c75-a198-e6e69c33aaeb · outbound

This paper cites Federated learning for medical image analysis: A survey,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Federated learning for medical image analysis: A survey,

Reference 5

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Observation 9c2c1aa7-558f-4dae-92fb-db47eab7ff08 · outbound

This paper cites Personalized federated learning under mixture of distribu- tions,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Personalized federated learning under mixture of distribu- tions,

Reference 6

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Observation 82a0ca77-eddb-45b9-b2ea-8c3672bd6a13 · outbound

This paper cites Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences,

Reference 7

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Observation c6f01b60-b6f9-4c02-84ae-de72c3b6c1ef · outbound

This paper cites Joint air-ground distributed federated learning for intelligent transportation systems,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Joint air-ground distributed federated learning for intelligent transportation systems,

Reference 8

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Observation 556b0e54-ec2c-42c2-aa2f-d6bb36fc3c21 · outbound

This paper cites Federated learning for smart cities: A comprehensive survey,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Federated learning for smart cities: A comprehensive survey,

Reference 9

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Observation abe63e57-fd30-47e5-8a3b-e89717beedc7 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learn- ing,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Scaffold: Stochastic controlled averaging for federated learn- ing,

Reference 10

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Observation f882e527-2abb-467a-bf9d-82cfb8097416 · outbound

This paper cites Feder- ated learning with compression: Unified analysis and sharp guarantees,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Feder- ated learning with compression: Unified analysis and sharp guarantees,

Reference 11

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Observation 40ab601c-538e-410a-adda-261101d09562 · outbound

This paper cites Fedfisher: Leveraging fisher information for one-shot federated learning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Fedfisher: Leveraging fisher information for one-shot federated learning,

Reference 12

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Observation 971e1621-7d2c-4a8f-b4c5-88e117c665b8 · outbound

This paper cites One-shot federated learning for leo con- stellations that reduces convergence time from days to 90 minutes,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning One-shot federated learning for leo con- stellations that reduces convergence time from days to 90 minutes,

Reference 13

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Observation a34ca80e-be6a-4a24-9e1a-992871357cf1 · outbound

This paper cites Dense: Data-free one-shot federated learning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Dense: Data-free one-shot federated learning,

Reference 14

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Observation 4c5e3654-ed26-4dad-b369-75f922d5faba · outbound

This paper cites One-shot empirical privacy estimation for feder- ated learning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning One-shot empirical privacy estimation for feder- ated learning,

Reference 15

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Observation a8fdaac9-bfd7-4879-968e-b1a89f97eff7 · outbound

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

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Fedproto: Federated prototype learning across heterogeneous clients,

Reference 16

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Observation 2431a032-db79-4b46-95ba-b7ca238f6f54 · outbound

This paper cites Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in federated learning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in federated learning,

Reference 17

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Observation 954f8511-832a-4d28-aa15-e18947e57e8a · outbound

This paper cites Learn- gene: From open-world to your learning task,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Learn- gene: From open-world to your learning task,

Reference 18

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Observation 634830be-7d60-465b-b20b-bfcb2a9fe416 · outbound

This paper cites Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models

Reference 19

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Observation f01e2180-a315-4bdc-8225-891e0dadbacd · outbound

This paper cites Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces,

Reference 20

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Observation a6254763-9869-4820-82b2-93afb4d48292 · outbound

This paper cites Cross-silo prototypical calibration for federated learning with non-iid data,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Cross-silo prototypical calibration for federated learning with non-iid data,

Reference 21

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Observation 80c7e39c-5129-4a2c-9755-e7f6d142f4da · outbound

This paper cites Dafkd: Domain-aware federated knowledge distillation,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Dafkd: Domain-aware federated knowledge distillation,

Reference 22

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Observation 1953f14b-13cd-49c8-8c06-56a6ed711db4 · outbound

This paper cites FedProc: Prototypical Contrastive Federated Learning on Non-IID data.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning FedProc: Prototypical Contrastive Federated Learning on Non-IID data

Reference 23

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Observation 89550cef-ae65-4f32-8029-2d675457d5de · outbound

This paper cites Federated learning with hierarchical clustering of local updates to improve training on non-iid data,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Federated learning with hierarchical clustering of local updates to improve training on non-iid data,

Reference 24

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Observation d201b03b-02f4-459c-a3bd-a53a9d834f62 · outbound

This paper cites Preservation of the global knowledge by not-true distillation in federated learning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Preservation of the global knowledge by not-true distillation in federated learning,

Reference 25

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Observation 682b75f5-03b7-4ba9-b78b-82308128ef8f · outbound

This paper cites Fedcompass: Efficient cross-silo federated learning on heterogeneous client devices using a computing power-aware scheduler,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Fedcompass: Efficient cross-silo federated learning on heterogeneous client devices using a computing power-aware scheduler,

Reference 26

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Observation 6c4f87c8-5d22-4758-a156-89c75c297477 · outbound

This paper cites Fedpe: Adaptive model pruning-expanding for federated learning on mobile devices,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Fedpe: Adaptive model pruning-expanding for federated learning on mobile devices,

Reference 27

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Observation 161b2750-1066-4ef4-bd5f-dd660dabd860 · outbound

This paper cites Com- putation and communication efficient federated learning with adaptive model pruning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Com- putation and communication efficient federated learning with adaptive model pruning,

Reference 28

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Observation 21b01978-cf04-4f09-90d2-58975962cb86 · outbound

This paper cites Complement sparsification: Low-overhead model pruning for federated learning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Complement sparsification: Low-overhead model pruning for federated learning,

Reference 29

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Observation 74cce464-6359-4095-8852-5485e48fb4e9 · outbound

This paper cites Efficient federated learning with enhanced privacy via lottery ticket pruning in edge computing,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Efficient federated learning with enhanced privacy via lottery ticket pruning in edge computing,

Reference 30

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Observation e443584b-3474-4a48-98b7-d968a17c2b1c · outbound

This paper cites Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Reference 31

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Observation 7cb15e4d-01bb-4c95-9775-a7266501d2d4 · outbound

This paper cites Fedlps: Heterogeneous federated learning for multiple tasks with local parameter sharing,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Fedlps: Heterogeneous federated learning for multiple tasks with local parameter sharing,

Reference 32

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Observation 5bac3da1-a3fe-409e-9772-7758b8d6c1db · outbound

This paper cites Linearly decomposing and recomposing vision transformers for diverse-scale models,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Linearly decomposing and recomposing vision transformers for diverse-scale models,

Reference 33

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Observation b5f9f43b-ecb0-4dd8-99aa-f3a726bb41b3 · outbound

This paper cites Initializing variable-sized vision transformers from learngene with learnable transformation,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Initializing variable-sized vision transformers from learngene with learnable transformation,

Reference 34

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Observation 35ff17e9-369c-4a9b-8028-3fb4a872e3e1 · outbound

This paper cites Trans- former as linear expansion of learngene,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Trans- former as linear expansion of learngene,

Reference 35

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Observation 948dce13-51d5-4c48-afcd-fffe80c4d17f · outbound

This paper cites Vision transformers as probabilistic expansion from learngene,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Vision transformers as probabilistic expansion from learngene,

Reference 36

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Observation b45f25df-5181-47c8-9a41-54f54ef77c83 · outbound

This paper cites Cluster-learngene: In- heriting adaptive clusters for vision transformers,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Cluster-learngene: In- heriting adaptive clusters for vision transformers,

Reference 37

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Observation 0442d650-042e-4daa-879e-b64d105e42ff · outbound

This paper cites Facilitating ai-based csi feedback deployment in massive mimo systems with learngene,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Facilitating ai-based csi feedback deployment in massive mimo systems with learngene,

Reference 38

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Observation b43cea1b-bd8e-41a7-b3f0-081e8798a1fe · outbound

This paper cites WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models

Reference 39

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Observation 3c696693-ca2e-4b58-8754-63cd58b44e51 · outbound

This paper cites KIND: Knowledge Integration and Diversion for Training Decomposable Models.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning KIND: Knowledge Integration and Diversion for Training Decomposable Models

Reference 40

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Observation dc2cbf17-5ba0-42a1-97ff-b4127b145e25 · outbound

This paper cites Building variable-sized models via learngene pool,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Building variable-sized models via learngene pool,

Reference 41

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Observation 052cc47c-a4d4-40f3-ac3c-4f9491054ef3 · outbound

This paper cites Transferring Core Knowledge via Learngenes.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Transferring Core Knowledge via Learngenes

Reference 42

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Observation 713413d5-61f5-4e26-ae81-99b339be5685 · outbound

This paper cites Objective assessment of image quality. ii. fisher information, fourier crosstalk, and figures of merit for task performance,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Objective assessment of image quality. ii. fisher information, fourier crosstalk, and figures of merit for task performance,

Reference 43

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Observation c98a8011-b373-460c-a346-dfcfdac8c302 · outbound

This paper cites A tutorial on fisher information,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning A tutorial on fisher information,

Reference 44

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Observation 40809115-5c68-4626-9633-57b95e78644e · outbound

This paper cites The adversarial attack and detection under the fisher information metric,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning The adversarial attack and detection under the fisher information metric,

Reference 45

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Observation c15c056e-a1b2-4126-bf73-6303fe13ef39 · outbound

This paper cites Neural fim for learning fisher information metrics from point cloud data,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Neural fim for learning fisher information metrics from point cloud data,

Reference 46

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Observation a3e735a9-a5e1-4616-8289-a1109bd82e6b · outbound

This paper cites Towards a theoretical and practical understanding of one-shot federated learning with fisher infor- mation,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Towards a theoretical and practical understanding of one-shot federated learning with fisher infor- mation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:20.038371Z

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Observation 6b90ba55-a80d-423d-b226-348195f10abb · outbound

This paper cites Use and abuse of the fisher information matrix in the assessment of gravitational-wave parameter-estimation prospects,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Use and abuse of the fisher information matrix in the assessment of gravitational-wave parameter-estimation prospects,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:20.021967Z

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Observation 2493d105-1f7f-4eb0-a72a-f885f389c4c4 · outbound

This paper cites Layer-wise relevance propagation for neural networks with local renor- malization layers,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Layer-wise relevance propagation for neural networks with local renor- malization layers,

Reference 49

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Observation bbd828de-38b2-4537-b34d-87ca9d2360de · outbound

This paper cites Elastic weight removal for faithful and abstractive dialogue generation,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Elastic weight removal for faithful and abstractive dialogue generation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:19.993719Z

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Observation 314f08ae-8fba-49a5-af09-ebaf0e2ac63a · outbound

This paper cites Beyond symmetry: Best submatrix selection for the sparse truncated svd,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Beyond symmetry: Best submatrix selection for the sparse truncated svd,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:19.976782Z

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Observation c909e60c-e0ee-4a39-a2fc-d47452651e28 · outbound

This paper cites Deep leakage from gradients,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Deep leakage from gradients,

Reference 52

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Observation 2f506d94-04bc-4170-9a89-4e5ba76114ce · outbound

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

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:19.699026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13155154-2c65-4f27-8866-ac8eea616948 · outbound

This paper cites Partialfed: Cross-domain personalized federated learning via partial initialization,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Partialfed: Cross-domain personalized federated learning via partial initialization,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:19.704050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b3bd63c-4cd5-43dc-b126-6b93e752668d · outbound

This paper cites Reading digits in natural images with unsupervised feature learning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Reading digits in natural images with unsupervised feature learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:19.941207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4f8f7293-9e58-4d3e-9fe0-eeef8c9972fe · outbound

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

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Learning multiple layers of features from tiny images,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:19.924723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 73e45132-cdb6-4779-8eca-6e83d0558493 · outbound

This paper cites FedBN: Federated Learning on Non-IID Features via Local Batch Normalization.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:19.718259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e8dd7d2-63eb-4942-b74d-2a6ba3c04000 · outbound

This paper cites Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:50:19.905919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bdc7de96-e3a0-4d42-8f42-ee612034c180 · outbound

This paper cites Deep residual learning for image recognition,.

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning Deep residual learning for image recognition,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:19.727378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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