Paper Citation Record · LEDGER

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2501.15486.

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

pith.paper-citation-record.v1
2501.15486 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:18:19.586301Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T18:44:45.397374Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T06:15:37.500328Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy29
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fae8eb2a-4000-443c-bb6d-712d3898fd37 · outbound

This paper cites Wasserstein generative adversarial networks.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Wasserstein generative adversarial networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:18.751041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee8411d7-71dd-4a0d-8352-feaaa643fb45 · outbound

This paper cites Federated domain generalization for image recognition via cross-client style transfer.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Federated domain generalization for image recognition via cross-client style transfer

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.993468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 959633a3-e7fa-42aa-afc6-07226b09e529 · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Unsupervised domain adaptation by backpropagation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.981518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e46dd1fb-9695-4aab-bba2-595856cf9c27 · outbound

This paper cites Domain-adversarial training of neural networks.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Domain-adversarial training of neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.922910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3d08a52b-350f-44de-9c76-dcd2108832e2 · outbound

This paper cites Dlow: Domain flow for adaptation and generalization.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Dlow: Domain flow for adaptation and generalization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.808249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation af3233ec-6233-474e-a894-ac89e79d84a2 · outbound

This paper cites Caltech-256 object category dataset.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Caltech-256 object category dataset

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.748319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bb7908c7-d89f-456e-9275-8132f31f7886 · outbound

This paper cites Out-of-distribution generalization of federated learning via implicit invariant relationships.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Out-of-distribution generalization of federated learning via implicit invariant relationships

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.738165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 73fd53e3-5d8a-4f10-88a1-3782011345d8 · outbound

This paper cites Arbitrary style transfer in real-time with adaptive instance normalization.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Arbitrary style transfer in real-time with adaptive instance normalization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.725354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f9538a61-1740-4927-9386-cbd282e5cb19 · outbound

This paper cites Deeper, broader and artier domain generalization.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Deeper, broader and artier domain generalization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.714999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7c096b8b-9e89-4bc3-87ed-2ae1907d3ef1 · outbound

This paper cites A survey on federated learning systems: Vision, hype and reality for data privacy and protection.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment A survey on federated learning systems: Vision, hype and reality for data privacy and protection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.703862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b12f2507-d35b-48bb-8829-9a50989a3318 · outbound

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

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Communication-efficient learning of deep networks from decentralized data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.003553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0076a503-ffef-493e-9602-05337c64fccb · outbound

This paper cites Zero-shot knowledge transfer via adversarial belief matching.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Zero-shot knowledge transfer via adversarial belief matching

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.685857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bbaa9235-c078-4bc2-932b-e7c3a96c9ba5 · outbound

This paper cites A survey on security and privacy of federated learning.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment A survey on security and privacy of federated learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.675278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b96fa159-9d18-4b58-9843-248de8ce9267 · outbound

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

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Fedsr: A simple and effective domain generalization method for federated learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.609440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 08c3336b-422e-49ca-aaf6-c03cde2f7b95 · outbound

This paper cites Domain generalization with interpolation robustness.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Domain generalization with interpolation robustness

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.538513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6459bcf-7218-45e3-81ee-377d6fedb0db · outbound

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

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Stablefdg: style and attention based learning for federated domain generalization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.456945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f51a9878-60b6-464e-ba12-d0acd06c8b03 · outbound

This paper cites Federated Adversarial Domain Adaptation.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Federated Adversarial Domain Adaptation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.032926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca6e8bda-2780-4af6-a5ec-7ac0f9f52f24 · outbound

This paper cites Do imagenet classifiers generalize to imagenet? In International conference on machine learning , pages 5389--5400.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Do imagenet classifiers generalize to imagenet? In International conference on machine learning , pages 5389--5400

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.041553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feaa8f11-9446-4f60-a0d2-6bc59b4e2e37 · outbound

This paper cites Model-based domain generalization.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Model-based domain generalization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.277742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ce5da9b9-0e4b-46d8-9c9e-cbd1ad7cd5a0 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.049305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa1fca8a-63ec-472d-88a9-e9cf73259da1 · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Deep coral: Correlation alignment for deep domain adaptation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.244742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ead98231-0e2c-4f98-912e-e53e093113ed · outbound

This paper cites Deep Domain Confusion: Maximizing for Domain Invariance.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Deep Domain Confusion: Maximizing for Domain Invariance

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.057261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f0922e1-157c-42ae-953d-069e40b510f2 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Deep hashing network for unsupervised domain adaptation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.233116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 781dee70-b173-413f-9552-644efdf52f99 · outbound

This paper cites Addressing model vulnerability to distributional shifts over image transformation sets.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Addressing model vulnerability to distributional shifts over image transformation sets

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.221848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4d9dd727-7ca1-4c98-ad54-fcb9dd21d2f7 · outbound

This paper cites Generalizing to unseen domains via adversarial data augmentation.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Generalizing to unseen domains via adversarial data augmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.210809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e8d7fe0c-627d-4325-8432-00a914a58555 · outbound

This paper cites Visual domain adaptation with manifold embedded distribution alignment.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Visual domain adaptation with manifold embedded distribution alignment

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.200937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3fb6eb7e-d0f5-45c8-9191-dc6eeb5b5281 · outbound

This paper cites Transfer learning with dynamic distribution adaptation.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Transfer learning with dynamic distribution adaptation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.190623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 90fca822-187f-4fd7-95b5-d7c0fb52c19c · outbound

This paper cites Robust and Generalizable Visual Representation Learning via Random Convolutions.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Robust and Generalizable Visual Representation Learning via Random Convolutions

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.306565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40014585-99ed-44b0-9350-23501dd8896b · outbound

This paper cites Federated adversarial domain hallucination for privacy-preserving domain generalization.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Federated adversarial domain hallucination for privacy-preserving domain generalization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.180491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9fcb2197-c56f-48c8-a19b-74c5dab7914e · outbound

This paper cites Fda: Fourier domain adaptation for semantic segmentation.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Fda: Fourier domain adaptation for semantic segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.097522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6433bd8-8928-4e06-8b76-445c991b30cc · outbound

This paper cites Federated multi-target domain adaptation.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Federated multi-target domain adaptation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:20.006644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation db7ae9c5-4a94-4112-8ba2-f5a181576200 · outbound

This paper cites FedMix: Approximation of Mixup under Mean Augmented Federated Learning.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment FedMix: Approximation of Mixup under Mean Augmented Federated Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.517940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0845ca6f-5691-40e7-b4d9-799730f61c5e · outbound

This paper cites mixup: Beyond empirical risk minimization.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment mixup: Beyond empirical risk minimization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:19.922984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b7d4630c-12f5-47f8-b913-6e96ad778d4f · outbound

This paper cites Federated Learning with Domain Generalization.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Federated Learning with Domain Generalization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.557534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35726298-8d88-48fd-afd9-6fda88d606f5 · outbound

This paper cites Federated domain generalization with generalization adjustment.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Federated domain generalization with generalization adjustment

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:19.761538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 23cf8459-c376-49af-93a9-5d1bb5a299c2 · outbound

This paper cites Domain adaptive ensemble learning.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Domain adaptive ensemble learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:19.722035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a6891bbf-3aa0-460b-aa80-de029dc262ac · outbound

This paper cites Domain generalization: A survey.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Domain generalization: A survey

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:19.709338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 439aab0b-0b66-42b7-a27d-6bfa1b2b565c · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Unpaired image-to-image translation using cycle-consistent adversarial networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:18:19.696984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5213e2f8-f886-4f3f-94d5-bc00495757af · outbound

This paper cites write newline.

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment write newline

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T14:18:19.586301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 307be597-10e5-4dbc-b7d2-8c60977aace2 · inbound

MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation cites this paper.

MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:15:37.507731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.