Paper Citation Record · LEDGER

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices

As of 12 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2412.05823.

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

pith.paper-citation-record.v1
2412.05823 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:24:32.766151Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:49:41.949729Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T22:49:42.425623Z

Reference resolution

57 of 57 outbound references displayed

verified exact1
verified fuzzy33
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4f10b9d9-c91d-41ba-b77c-e34392e52948 · outbound

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

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Federated learning in mobile edge networks: A comprehensive survey

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.641575Z

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 4c047064-93e5-4294-81db-72b52de37ada · outbound

This paper cites Advances and open problems in federated learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Advances and open problems in federated learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.505781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60ad7d9e-5071-4cd9-b08e-866606be68e6 · outbound

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

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Communication-efficient learning of deep networks from decentralized data

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.511123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a3b114f-0a0c-46af-9f83-b4eb2c054dc7 · outbound

This paper cites A Survey on Heterogeneous Federated Learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices A Survey on Heterogeneous Federated Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.515899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5f10a98-51e1-46e7-a90b-dde738206627 · outbound

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

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.605113Z

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 fca926d8-2e9c-40e0-81c0-7a558b98affd · outbound

This paper cites Fedgh: Heterogeneous federated learning with generalized global header.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Fedgh: Heterogeneous federated learning with generalized global header

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.590030Z

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 591d7428-d3c8-4726-97f8-9569b2fa1c72 · outbound

This paper cites Federated Learning with Domain Generalization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Federated Learning with Domain Generalization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.532100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 717ce887-26b7-44a7-a66b-f6d281b27099 · outbound

This paper cites Benchmarking algorithms for federated domain generalization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Benchmarking algorithms for federated domain generalization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.574102Z

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 b3bfaf35-97f9-4fa3-8d18-9eebab7526ca · outbound

This paper cites Stablefdg: Style and attention based learning for federated domain generalization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Stablefdg: Style and attention based learning for federated domain generalization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.556979Z

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 a1e57c27-5ab5-4d22-9b4d-83089d926f2d · outbound

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

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.546381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dad2b6c7-8dba-4227-a474-6fccf3c6c873 · outbound

This paper cites Fedmp: Federated learning through adaptive model pruning in heterogeneous edge computing.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Fedmp: Federated learning through adaptive model pruning in heterogeneous edge computing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.541672Z

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 1f81c8f2-4b18-4dfe-b08c-6fbff691885c · outbound

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

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.557619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f78fa3c-c3bf-4062-9400-518f68004de3 · outbound

This paper cites Exact feature distribution matching for arbitrary style transfer and domain generalization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Exact feature distribution matching for arbitrary style transfer and domain generalization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.525999Z

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 13476bee-096f-4b4e-aa8f-7665f18497f4 · outbound

This paper cites Fedsr: A simple and effective domain generalization method for federated learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Fedsr: A simple and effective domain generalization method for federated learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.511064Z

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 d6b0d9f3-ca08-4a40-8ec9-a9bb287320c7 · outbound

This paper cites Rethinking federated learning with domain shift: A prototype view.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Rethinking federated learning with domain shift: A prototype view

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.496502Z

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 79e67ba9-3347-4ce2-a401-8601fd7e2f05 · outbound

This paper cites Model-contrastive federated learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Model-contrastive federated learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.575907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 318bdab4-d12e-49f4-b66e-5d4c008a3d80 · outbound

This paper cites Federated optimization in heterogeneous networks.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Federated optimization in heterogeneous networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.580455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a16f391-d87d-4283-a214-2bebde094c53 · outbound

This paper cites HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.584969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d8fd551-0115-4764-81f5-945ffb1fda35 · outbound

This paper cites Data-free knowledge distillation for heteroge- neous federated learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Data-free knowledge distillation for heteroge- neous federated learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.589706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d65598de-2bfe-4715-a045-18610be7b5de · outbound

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

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Fedproto: Federated prototype learning across heterogeneous clients

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.594276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a44a6ee5-ec45-4d5c-9a33-aedc5e9b0914 · outbound

This paper cites Hermes: an efficient federated learning framework for heterogeneous mobile clients.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Hermes: an efficient federated learning framework for heterogeneous mobile clients

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.440383Z

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 7befbec2-4b81-490a-abd2-902eade7ac26 · outbound

This paper cites Leung, and Leandros Tassiulas.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Leung, and Leandros Tassiulas

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.424045Z

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 835908e9-0132-4fa4-9d37-1741f3ae5509 · outbound

This paper cites One-Shot Pruning for Fast-adapting Pre-trained Models on Devices.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices One-Shot Pruning for Fast-adapting Pre-trained Models on Devices

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:24:32.861203Z

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 20b68ca7-45da-41e5-9083-27dbcd63912a · outbound

This paper cites SCAFFOLD: Stochastic controlled averaging for federated learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices SCAFFOLD: Stochastic controlled averaging for federated learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.409047Z

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 86e09405-12f8-459d-a862-b7a87ea2baed · outbound

This paper cites Vincent Poor.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Vincent Poor

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.393167Z

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 163ac29e-e1d2-40e5-a115-8c230b8580d1 · outbound

This paper cites Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.621957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23969e3f-7cc8-4e1a-a364-aedd88abd200 · outbound

This paper cites Every parameter matters: Ensuring the convergence of federated learning with dynamic heterogeneous models reduction.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Every parameter matters: Ensuring the convergence of federated learning with dynamic heterogeneous models reduction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.376766Z

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 f5ada0d7-322a-4925-abe9-c1dec5d6411b · outbound

This paper cites Splitfed: When federated learning meets split learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Splitfed: When federated learning meets split learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.631488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56f55516-6258-4dfa-bc7a-7ba158e00841 · outbound

This paper cites Split learning over wireless networks: Parallel design and resource management.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Split learning over wireless networks: Parallel design and resource management

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.349577Z

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 e070424a-daea-4228-b6a2-a4109aa8761f · outbound

This paper cites Binarizing split learning for data privacy enhancement and computation reduction.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Binarizing split learning for data privacy enhancement and computation reduction

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.333210Z

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 f19f7900-8a66-4787-8e93-4995097ccf2d · outbound

This paper cites Domain generalization: A survey.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Domain generalization: A survey

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.317910Z

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 6a18f125-31b6-428f-98a2-ce15400555a2 · outbound

This paper cites Domain generalization via conditional invariant representations.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Domain generalization via conditional invariant representations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.301508Z

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 072b3588-c0ba-4599-8e55-a2997511b86d · outbound

This paper cites Domain gen- eralization via entropy regularization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Domain gen- eralization via entropy regularization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.285266Z

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 2adc89c5-e1e5-443a-a9ea-66f5d1bdfe3f · outbound

This paper cites Respecting domain relations: Hypothesis invariance for domain generalization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Respecting domain relations: Hypothesis invariance for domain generalization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.268450Z

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 43d5f714-0927-4e9b-b8bf-acc4f9d78810 · outbound

This paper cites Learning to generalize: Meta- learning for domain generalization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Learning to generalize: Meta- learning for domain generalization

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.252649Z

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 36c5bbf6-c6b9-4f5b-a2ab-e36866d3a71c · outbound

This paper cites Metareg: Towards domain generalization using meta-regularization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Metareg: Towards domain generalization using meta-regularization

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.236682Z

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 7326c80f-d46e-429e-95fc-698f42e2fe4d · outbound

This paper cites Cooperative pruning in cross-domain deep neural network compression.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Cooperative pruning in cross-domain deep neural network compression

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.220301Z

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 9598781c-aeeb-4e2e-a380-f1330aa1f48a · outbound

This paper cites Learning to generalize unseen domains via memory-based multi-source meta-learning for person re-identification.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Learning to generalize unseen domains via memory-based multi-source meta-learning for person re-identification

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.204634Z

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 267f97ad-eba8-43e0-af78-be08185735d8 · outbound

This paper cites Domain generalization with mixstyle.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Domain generalization with mixstyle

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.681185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db350128-e5c7-4647-ab17-de678c7ea575 · outbound

This paper cites Uncertainty modeling for out-of-distribution generalization.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Uncertainty modeling for out-of-distribution generalization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.685686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fcf7d2c-6e2a-4dfc-9def-b82ab1fa33d0 · outbound

This paper cites Learn from others and be yourself in heterogeneous federated learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Learn from others and be yourself in heterogeneous federated learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.165619Z

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 2373da06-b7bd-4cf3-abf3-0974d5a4fe12 · outbound

This paper cites A comprehensive survey on transfer learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices A comprehensive survey on transfer learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.148717Z

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 c4cdc030-2697-4dd9-915a-418568d8b5b0 · outbound

This paper cites A survey of transfer learning.Journal of Big data, 3(1):1–40, 2016.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices A survey of transfer learning.Journal of Big data, 3(1):1–40, 2016

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.699405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f37a6aeb-b81a-4e90-815d-92b5980df270 · outbound

This paper cites How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems, volume 27.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems, volume 27

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.119117Z

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 a9b06e7c-52a2-482d-8744-60754187766e · outbound

This paper cites Eliminating domain bias for federated learning in representation space.Advances in Neural Information Processing Systems, 36, 2024.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Eliminating domain bias for federated learning in representation space.Advances in Neural Information Processing Systems, 36, 2024

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.102635Z

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 49eb989d-e0ae-4a3b-8d10-5bd907281527 · outbound

This paper cites Mnasnet: Platform-aware neural architecture search for mobile.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Mnasnet: Platform-aware neural architecture search for mobile

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.713128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b718562c-0f0e-4351-9602-b2b4bd220590 · outbound

This paper cites Learning efficient convolutional networks through network slimming.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Learning efficient convolutional networks through network slimming

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.077340Z

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 d664734b-ac21-40b8-b990-5b71f8d13fdd · outbound

This paper cites Pruning filters for efficient convnets.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Pruning filters for efficient convnets

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.722692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a642547c-77c7-4f49-b137-e72d704e5f2d · outbound

This paper cites FedML: A Research Library and Benchmark for Federated Machine Learning.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.727299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14166f72-5323-426a-a054-82ef9f9862a3 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Pytorch: An imperative style, high-performance deep learning library

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.732267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0a4b738-6ea4-4326-84c6-3731ad81bbb3 · outbound

This paper cites Resource-adaptive federated learning with all-in-one neural composition.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Resource-adaptive federated learning with all-in-one neural composition

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:33.041189Z

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 187a8222-47fd-47f9-a801-6f630b4d97a7 · outbound

This paper cites Gradient-based learning applied to document recognition.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Gradient-based learning applied to document recognition

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.741748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a50fd09-540a-489f-8deb-50a055ceb0cb · outbound

This paper cites an unresolved cited work.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:24:33.012586Z

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 1e68be5b-edd8-4901-89fa-cf8c261b162a · outbound

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

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Reading digits in natural images with unsupervised feature learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:32.996066Z

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 3dcebc09-49e1-4677-ae5a-d46542b3abcb · outbound

This paper cites Effects of Degradations on Deep Neural Network Architectures.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Effects of Degradations on Deep Neural Network Architectures

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T20:24:32.756264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4db774d-7961-4da2-bea6-5bae65cbab46 · outbound

This paper cites Geodesic flow kernel for unsupervised domain adaptation.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Geodesic flow kernel for unsupervised domain adaptation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:32.980399Z

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 1ba8e6b8-fbff-44cd-807d-65a78c2433e3 · outbound

This paper cites Deep residual learning for image recognition.

DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices Deep residual learning for image recognition

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:24:32.964502Z

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.

Pith citing papers

Observation 4134b9fc-858c-4e14-9649-49dd7a84fb63 · inbound

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices cites this paper.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:49:42.430668Z

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.