Paper Citation Record · LEDGER

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization

As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.09385.

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

pith.paper-citation-record.v1
2505.09385 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:39:35.656116Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation 082de249-8ff4-4f00-9819-4a5978954dfd · outbound

This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization SegDiff: Image Segmentation with Diffusion Probabilistic Models

Reference 1

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Observation a69f75ec-7028-4df9-a3f0-b1dd1d6e9f1f · outbound

This paper cites The cityscapes dataset for semantic urban scene under- standing.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization The cityscapes dataset for semantic urban scene under- standing

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.207621Z

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Observation af2cf181-13b6-470e-965b-5b7a7bed2b37 · outbound

This paper cites Preserving privacy in federated learning with ensemble cross-domain knowledge distilla- tion.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Preserving privacy in federated learning with ensemble cross-domain knowledge distilla- tion

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.155846Z

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Observation c221f478-064f-4ca7-9523-6901f78d2a23 · outbound

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

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Rethinking federated learning with domain shift: A prototype view

Reference 9

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Observation 8c36c265-b258-445a-b7f6-b42bd504d278 · outbound

This paper cites pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving

Reference 11

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Source-reported events for the cited work

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Observation 02c6559a-5565-43a0-943d-5a8c762b0bfb · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Swin transformer: Hierarchical vision transformer using shifted windows

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.096407Z

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Observation 11dcbc1e-8997-4979-8820-13fa9a341061 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Fully convolutional networks for semantic segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.081638Z

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Observation af659969-cc9c-45d5-b84b-ab5e525df1ba · outbound

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

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Communication-efficient learning of deep networks from decentralized data

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:39:35.545845Z

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Observation 991b8927-1def-423d-abe9-e4f41c8e658a · outbound

This paper cites Fedseg: Class-heterogeneous federated learning for semantic segmentation.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Fedseg: Class-heterogeneous federated learning for semantic segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.045703Z

Source-reported events for the cited work

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Observation 758a33a0-6bcf-4d1e-b5b7-3963335eb95d · outbound

This paper cites Prototype Guided Federated Learning of Visual Feature Representations.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Prototype Guided Federated Learning of Visual Feature Representations

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T21:39:35.555053Z

Source-reported events for the cited work

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Observation 01f791c4-886b-45bf-9792-4bffc17b069f · outbound

This paper cites The mapillary vistas dataset for semantic understanding of street scenes.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization The mapillary vistas dataset for semantic understanding of street scenes

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.031935Z

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Observation 2e744e7c-dbeb-48ce-b412-d31825464e25 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library.Advances in neural in- formation processing systems, 32,.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Pytorch: An imperative style, high- performance deep learning library.Advances in neural in- formation processing systems, 32,

Reference 20

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Source-reported events for the cited work

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Observation 1a111db9-6acf-43c4-a9fd-89a4e451dc60 · outbound

This paper cites Playing for data: Ground truth from computer games.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Playing for data: Ground truth from computer games

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.996029Z

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Observation f1650c0e-c08d-4e08-984f-cbe16d6a16f3 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization U-net: Convolutional networks for biomedical image segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.982365Z

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Observation 41da7eaf-ea76-4c6c-8c01-458a3c2e4c72 · outbound

This paper cites The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.968626Z

Source-reported events for the cited work

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Observation 7df7d139-6060-4c32-af3d-3253361fe2d8 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.955192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 75ccc932-b8c3-413c-a1e2-c6226d0836d7 · outbound

This paper cites Amp: Adaptive masked prox- ies for few-shot segmentation.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Amp: Adaptive masked prox- ies for few-shot segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.941822Z

Source-reported events for the cited work

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Observation 6a9f3070-d556-4d5b-86b7-12834fd20293 · outbound

This paper cites Federated adaptive prompt tuning for multi-domain collaborative learning.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Federated adaptive prompt tuning for multi-domain collaborative learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.928247Z

Source-reported events for the cited work

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Observation 305d7bd7-c1f3-4dab-80f8-079eaa77f1b9 · outbound

This paper cites Towards personalized federated learn- ing.IEEE transactions on neural networks and learning systems, 34(12):9587–9603,.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Towards personalized federated learn- ing.IEEE transactions on neural networks and learning systems, 34(12):9587–9603,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.914123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4b2cb670-e56c-41de-a334-a4809434c565 · outbound

This paper cites Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation

Reference 29

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Observation a5623e9b-ebd6-4ebc-b401-31a829a356ec · outbound

This paper cites Diffuse attend and segment: Unsupervised zero-shot segmentation using stable diffusion.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Diffuse attend and segment: Unsupervised zero-shot segmentation using stable diffusion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.900718Z

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Observation 544bb9f8-27c0-425e-90bd-bb96b5927a1c · outbound

This paper cites Visualizing data using t-sne.Journal of Machine Learning Research, 9(11),.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Visualizing data using t-sne.Journal of Machine Learning Research, 9(11),

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.887158Z

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Observation 3dc97dc0-cdf1-4e75-8448-58781a91c3fc · outbound

This paper cites FedSiam-DA: Dual-aggregated federated learning via siamese network for non-IID data.IEEE Transactions on Mobile Comput- ing, 24(2):985–998,.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization FedSiam-DA: Dual-aggregated federated learning via siamese network for non-IID data.IEEE Transactions on Mobile Comput- ing, 24(2):985–998,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.857774Z

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Observation f4d5d585-e28b-4838-9128-7f2ff9312540 · outbound

This paper cites Segmamba: Long-range sequen- tial modeling mamba for 3d medical image segmentation.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Segmamba: Long-range sequen- tial modeling mamba for 3d medical image segmentation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.825483Z

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Observation 0b66abc7-f6c2-4a15-b226-4304ebf6147a · outbound

This paper cites Fblg: A local graph based approach for han- dling dual skewed non-iid data in federated learning.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Fblg: A local graph based approach for han- dling dual skewed non-iid data in federated learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.810552Z

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Observation dceb038f-0dec-41ba-818e-df41c87e2e95 · outbound

This paper cites Bdd100k: A diverse driv- ing dataset for heterogeneous multitask learning.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Bdd100k: A diverse driv- ing dataset for heterogeneous multitask learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.796431Z

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Observation 914d8653-3063-4513-a806-ec2961734e2a · outbound

This paper cites Bisenet v2: Bilateral network with guided aggregation for real- time semantic segmentation.International Journal of Computer Vision, 129:3051–3068,.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Bisenet v2: Bilateral network with guided aggregation for real- time semantic segmentation.International Journal of Computer Vision, 129:3051–3068,

Reference 38

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Observation 38b78eb7-7059-49ba-a2ac-44e86ebe3607 · outbound

This paper cites A review of deep learning methods for semantic seg- mentation of remote sensing imagery.Expert Systems with Applications, 169:114417,.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization A review of deep learning methods for semantic seg- mentation of remote sensing imagery.Expert Systems with Applications, 169:114417,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.766703Z

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Observation e02f21b7-5708-415c-a485-87ecdae69d4a · outbound

This paper cites Pyramid scene parsing network.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Pyramid scene parsing network

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.750956Z

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Observation 387552df-9d02-4aa9-9502-a67229132a50 · outbound

This paper cites LoveDA: A remote sens- ing land-cover dataset for domain adaptive semantic seg- mentation, October.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization LoveDA: A remote sens- ing land-cover dataset for domain adaptive semantic seg- mentation, October

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.872064Z

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Observation aecc6516-51c3-4c91-8ae4-e9da7ae6d76e · outbound

This paper cites Fedst: Federated style transfer learning for non-iid image segmentation.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Fedst: Federated style transfer learning for non-iid image segmentation

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.068292Z

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Observation 522fd259-74d7-468f-bf96-28ef2b5b03b3 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization An image is worth 16x16 words: Transformers for image recognition at scale

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T21:39:35.495279Z

Source-reported events for the cited work

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Observation 6dfda104-b595-480e-93b3-99227592aa82 · outbound

This paper cites Exploiting shared rep- resentations for personalized federated learning.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Exploiting shared rep- resentations for personalized federated learning

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.221432Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 87fdc078-752e-4726-bd99-df6f0792103f · outbound

This paper cites Med- ical image segmentation using deep semantic-based meth- ods: A review of techniques, applications and emerging trends.Information Fusion, 90:316–352,.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Med- ical image segmentation using deep semantic-based meth- ods: A review of techniques, applications and emerging trends.Information Fusion, 90:316–352,

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.009393Z

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Observation 9e197b3d-a3fd-418d-9118-af3ba15bbb63 · outbound

This paper cites Feddrive: Generalizing fed- erated learning to semantic segmentation in autonomous driving.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Feddrive: Generalizing fed- erated learning to semantic segmentation in autonomous driving

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.184545Z

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Observation db87d416-3c9e-45e8-8517-2da98bd45d41 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convo- lutional nets, atrous convolution, and fully connected crfs.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Deeplab: Semantic image segmentation with deep convo- lutional nets, atrous convolution, and fully connected crfs

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.235671Z

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FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:39:36.170083Z

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Observation c43b128f-eca0-4f2e-8f5d-758486977f4b · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Image-to-image translation with conditional adversarial networks

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.124821Z

Source-reported events for the cited work

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Observation 08976c6b-9f61-4d75-8093-40cdab6f5562 · outbound

This paper cites Federated optimization in heterogeneous networks.Pro- ceedings of Machine Learning and Systems, 2:429–450,.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization Federated optimization in heterogeneous networks.Pro- ceedings of Machine Learning and Systems, 2:429–450,

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:36.110921Z

Source-reported events for the cited work

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Observation 0bd23914-f978-47f8-9a1c-8173faafa6aa · outbound

This paper cites pflfe: Cross-silo per- sonalized federated learning via feature enhancement on medical image segmentation.

FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization pflfe: Cross-silo per- sonalized federated learning via feature enhancement on medical image segmentation

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:39:35.841265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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