Paper Citation Record · LEDGER

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment

As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations 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 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:42:12.415839Z

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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 d8b14bc9-1f88-493c-bf1f-a3faa0c404f5 · inbound

Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems cites this paper.

Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T18:42:12.415839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-15T06:32:42.880941+00:00.