Paper Citation Record · LEDGER

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning

As of 8 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2506.04071.

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

pith.paper-citation-record.v1
2506.04071 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:52:06.085397Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:52:05.987755Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:52:06.137780Z

Reference resolution

34 of 34 outbound references displayed

verified exact1
verified fuzzy27
unresolved4
parse uncertain2
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0d233c5-4304-4148-8d18-80ec4eda25aa · outbound

This paper cites an unresolved cited work.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:06.436815Z

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Observation b943468c-2088-4c7a-adae-310a1f190669 · outbound

This paper cites Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning

Reference 2

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Observation d6d0530c-95d3-49d9-853a-ca6a78c5b487 · outbound

This paper cites Federated Learning The goal of federated learning (FL) is to train a single (global) model to make accurate predictions across all agents in a net- work.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Federated Learning The goal of federated learning (FL) is to train a single (global) model to make accurate predictions across all agents in a net- work

Reference 3

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Observation ea30399c-6843-40ab-ac22-21de215f1386 · outbound

This paper cites We achieve this distribution-alignment goal by generating a tar- get space to which we project all local data.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning We achieve this distribution-alignment goal by generating a tar- get space to which we project all local data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.418904Z

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Observation 68c2f215-12f5-40ff-b98d-770084edd363 · outbound

This paper cites To demonstrate the advantages of using our preprocessing step, we require a learning algorithm.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning To demonstrate the advantages of using our preprocessing step, we require a learning algorithm

Reference 5

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Observation 945f05bb-6fbe-408d-b9d5-15392e902119 · outbound

This paper cites Comm. Rounds.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Comm. Rounds

Reference 6

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Observation 1d7a2b47-eb57-49eb-88a2-4a5a06df6162 · outbound

This paper cites an unresolved cited work.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Unresolved cited work

Reference 7

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Observation 2d7158d9-bb32-40ae-928a-864c2a278bcb · outbound

This paper cites We accomplish this by projecting local data to a space that encodes all local data, in turn minimizing the distributional discrepancy between agents.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning We accomplish this by projecting local data to a space that encodes all local data, in turn minimizing the distributional discrepancy between agents

Reference 8

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Observation 6697d553-a21a-4448-a773-e971b8d9cd2a · outbound

This paper cites Collaborative op- timization and aggregation for decentralized domain generalization and adaptation,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Collaborative op- timization and aggregation for decentralized domain generalization and adaptation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.261279Z

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Observation 751bf1b8-5552-43cd-96f0-e37f26fe5c69 · outbound

This paper cites an unresolved cited work.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Unresolved cited work

Reference 10

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Observation a051c305-130f-48b5-a122-1c793cca432d · outbound

This paper cites Therefore, we have O(M d2/ϵ2) for local barycenters and O(N d2/ϵ2) for the global barycenter.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Therefore, we have O(M d2/ϵ2) for local barycenters and O(N d2/ϵ2) for the global barycenter

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.354893Z

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Observation f25da86b-be7e-45ae-b7fe-34ae16f3b02d · outbound

This paper cites Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.242412Z

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Observation f5e5d2d9-723c-4a4d-9347-25612337e1a3 · outbound

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

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 13

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Observation f739e827-4977-44d6-b413-d2b60d38fa67 · outbound

This paper cites Towards personalized federated learning,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Towards personalized federated learning,

Reference 14

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Source-reported events for the cited work

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Observation 80fd8e3d-6b84-446e-8693-f252f787f85e · outbound

This paper cites Federated learning: Challenges, methods, and future directions,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Federated learning: Challenges, methods, and future directions,

Reference 15

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Observation 06f7850a-7c16-4a6b-bf0f-dcaac907ff66 · outbound

This paper cites Fedprof: Selective federated learning based on distri- butional representation profiling,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Fedprof: Selective federated learning based on distri- butional representation profiling,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.309261Z

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Observation 5b65007e-a13a-4138-a9d0-8c1f544bdd77 · outbound

This paper cites An optimal transport approach to personalized federated learning,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning An optimal transport approach to personalized federated learning,

Reference 17

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Observation b27be5b4-63bb-48fe-86a3-e30ce1b82f60 · outbound

This paper cites Regularized discrete optimal transport,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Regularized discrete optimal transport,

Reference 18

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Observation 284b1624-462f-4b34-8ab8-ab82c7d50799 · outbound

This paper cites Generalizing to unseen domains: A survey on domain generalization,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Generalizing to unseen domains: A survey on domain generalization,

Reference 19

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Observation fe0ece62-ebec-41be-a5dc-62f9dc6ccd75 · outbound

This paper cites Fedhealth: A federated transfer learning framework for wearable healthcare,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Fedhealth: A federated transfer learning framework for wearable healthcare,

Reference 20

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Observation ba9c6035-f508-4315-8e9f-abd65e0c3a32 · outbound

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Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Unresolved cited work

Reference 21

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Observation c2800948-9cc1-4efc-9784-b00a08d016ee · outbound

This paper cites Given n samples of dimension d and reg- ularization parameter ϵ, we have a complexity of O(nd2/ϵ2).

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Given n samples of dimension d and reg- ularization parameter ϵ, we have a complexity of O(nd2/ϵ2)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.383091Z

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Observation 673ba6e1-dd3b-4d27-88b2-f60f0fbc40a5 · outbound

This paper cites Federated Learning with Domain Generalization.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Federated Learning with Domain Generalization

Reference 23

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Observation fc66689c-ff95-4573-b3a2-e76f54e4ff5f · outbound

This paper cites Learning to generate novel domains for domain generalization,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Learning to generate novel domains for domain generalization,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.252160Z

Source-reported events for the cited work

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Observation cce86920-8cfe-4190-9459-0c04a988cfd2 · outbound

This paper cites Federated visual classification with real-world data dis- tribution,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Federated visual classification with real-world data dis- tribution,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.232582Z

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Observation 3f47db98-3bc1-4819-8d98-a27a3bf72e24 · outbound

This paper cites Personalized federated learning with theoretical guar- antees: A model-agnostic meta-learning approach,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Personalized federated learning with theoretical guar- antees: A model-agnostic meta-learning approach,

Reference 26

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Observation 57726d5b-5183-48a0-9b83-db3b5e8095a6 · outbound

This paper cites M ´emoire sur la th´eorie des d´eblais et des remblais,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning M ´emoire sur la th´eorie des d´eblais et des remblais,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.213581Z

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Observation 0cfe1d15-4fd2-4b5f-a43b-8fcbd6fffeb6 · outbound

This paper cites Fast computation of wasserstein barycenters,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Fast computation of wasserstein barycenters,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.204392Z

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 5a37cab9-5b1f-4a73-9c73-533ca63382e0 · outbound

This paper cites Sinkhorn distances: Lightspeed compu- tation of optimal transport,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Sinkhorn distances: Lightspeed compu- tation of optimal transport,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.194460Z

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Observation 7609d66b-761f-49c7-970e-5f471b88962a · outbound

This paper cites Iterative bregman pro- jections for regularized transportation problems,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Iterative bregman pro- jections for regularized transportation problems,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.183867Z

Source-reported events for the cited work

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Observation c9cf2055-f445-46bd-ab3f-6107e4bac110 · outbound

This paper cites Model- contrastive federated learning,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Model- contrastive federated learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.174112Z

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 bfd2b3b9-7e97-44a7-9bb2-e82c5205d7bb · outbound

This paper cites Federated Learning with Matched Averaging.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning Federated Learning with Matched Averaging

Reference 32

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Observation 0bb0a9bc-777b-437b-9420-293b94d865e5 · outbound

This paper cites No fear of heterogeneity: Classifier calibration for federated learning with non-iid data,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning No fear of heterogeneity: Classifier calibration for federated learning with non-iid data,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.163735Z

Source-reported events for the cited work

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Observation 4ecdcf03-5d1d-4d80-ac3b-df4b1e4fb8a2 · outbound

This paper cites On the complexity of approximating wasser- stein barycenters,.

Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning On the complexity of approximating wasser- stein barycenters,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:06.153948Z

Source-reported events for the cited work

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Pith citing papers

Observation b943468c-2088-4c7a-adae-310a1f190669 · inbound