Paper Citation Record · LEDGER

Heterogeneous Federated Learning with Prototype Alignment and Upscaling

As of 13 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.04310.

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

pith.paper-citation-record.v1
2507.04310 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:55:03.636394Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

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

46 of 46 outbound references displayed

verified exact0
verified fuzzy36
unresolved10
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c3bbd5c8-d8a5-4185-a420-f4e730549497 · outbound

This paper cites Deep Learning using Rectified Linear Units (ReLU).

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Deep Learning using Rectified Linear Units (ReLU)

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2fc4968-9ebe-4348-8dcd-03927d4132f8 · outbound

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

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Fe- drolex: Model-heterogeneous federated learning with rolling sub-model extraction

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.748932Z

Source-reported events for the cited work

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Observation bb2ca1ad-971c-4eeb-aee8-516ec3599d2f · outbound

This paper cites Angular visual hardness.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Angular visual hardness

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.731376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 952ca455-2c79-4412-abd7-0500530d31c6 · outbound

This paper cites Tackling data heterogeneity in federated learning with class prototypes.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Tackling data heterogeneity in federated learning with class prototypes

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.716129Z

Source-reported events for the cited work

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Observation 625b1257-3de0-4946-bd26-3d84a7206ce8 · outbound

This paper cites Hyperspherical Variational Auto-Encoders.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Hyperspherical Variational Auto-Encoders

Reference 5

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Source-reported events for the cited work

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Observation 2e6bb4cc-a8d9-45dd-9ac5-7f4713213ee5 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recog- nition.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Arcface: Additive angular margin loss for deep face recog- nition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.699833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6d50bd8c-8b61-4c4e-9ed4-3d29fc0789a6 · outbound

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

Heterogeneous Federated Learning with Prototype Alignment and Upscaling HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:55:02.489080Z

Source-reported events for the cited work

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Observation 2c13ca5b-f77a-4af0-8f50-59be7a3f3749 · outbound

This paper cites Fedhp: Federated learning with hyperspherical prototypical regular- ization.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Fedhp: Federated learning with hyperspherical prototypical regular- ization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.685557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 855b2703-dd25-477e-bcbc-692e426967f0 · outbound

This paper cites Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.669731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6baf5649-7def-49df-9195-482f9253722d · outbound

This paper cites Personalized cross-silo federated learning on non-iid data.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Personalized cross-silo federated learning on non-iid data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.652709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 45e27e4e-f956-41e0-9e92-1fabf91a6f1b · outbound

This paper cites Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:55:03.028693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8a5f50c-e513-400a-9f7f-45f679464982 · outbound

This paper cites Balanced open set domain adaptation via centroid alignment.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Balanced open set domain adaptation via centroid alignment

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.636558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 94fafb5b-ea56-4722-97e7-de5455a69997 · outbound

This paper cites Learning multiple layers of features from tiny images.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Learning multiple layers of features from tiny images

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:55:03.143386Z

Source-reported events for the cited work

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Observation 5d76c9ed-fb0d-4094-8258-96394634ebdc · outbound

This paper cites Tiny imagenet visual recognition challenge.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Tiny imagenet visual recognition challenge

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.614838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e8a084e9-06b3-4303-9b2a-9a1b910f8c3f · outbound

This paper cites FedMD: Heterogenous Federated Learning via Model Distillation.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling FedMD: Heterogenous Federated Learning via Model Distillation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:55:03.419039Z

Source-reported events for the cited work

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Observation 88d77742-868d-48ed-bd86-361deea8a157 · outbound

This paper cites Feder- ated learning on non-iid data silos: An experimental study.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Feder- ated learning on non-iid data silos: An experimental study

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.602347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6a4646f4-ab41-4767-b29a-394b8940f1e6 · outbound

This paper cites Think Locally, Act Globally: Federated Learning with Local and Global Representations.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Think Locally, Act Globally: Federated Learning with Local and Global Representations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T19:55:03.522793Z

Source-reported events for the cited work

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Observation a089de7e-b9c0-4c54-8137-0c72bf2c1d4f · outbound

This paper cites Regularizing neural networks via minimizing hyperspherical energy.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Regularizing neural networks via minimizing hyperspherical energy

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.590968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0cf1aa4e-94e8-4b1f-83d6-643b70e65e44 · outbound

This paper cites Ensemble distillation for robust model fusion in federated learning.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Ensemble distillation for robust model fusion in federated learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.579267Z

Source-reported events for the cited work

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Observation e6a22575-36aa-4085-82c1-9a3401c37c10 · outbound

This paper cites Large-Margin Softmax Loss for Convolutional Neural Networks.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Large-Margin Softmax Loss for Convolutional Neural Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T19:55:03.535482Z

Source-reported events for the cited work

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Observation 1445ea90-ab22-48dd-96e0-51972f2f4859 · outbound

This paper cites Deep hyperspherical learning.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Deep hyperspherical learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.567496Z

Source-reported events for the cited work

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Observation d52629af-2ce1-441c-bfe0-454632499418 · outbound

This paper cites Learning towards minimum hyper- spherical energy.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Learning towards minimum hyper- spherical energy

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.556041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 284ac42d-529e-4f5b-8ab2-8013b0c3a703 · outbound

This paper cites Orthogonal over-parameterized training.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Orthogonal over-parameterized training

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.513077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 04563e50-fa1e-4d7b-b0ed-cc1cb2e84074 · outbound

This paper cites Learning with hyperspherical uniformity.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Learning with hyperspherical uniformity

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.326109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 885c1689-a080-45fd-8de9-0686b5764a23 · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architec- ture design.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Shufflenet v2: Practical guidelines for efficient cnn architec- ture design

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.254343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d28e6dad-8d19-4c29-bb7e-105aa03a35e7 · outbound

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

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Communication- efficient learning of deep networks from decentralized data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.099002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f09c2fea-7d5b-41b3-9d8e-cd7b6221f05b · outbound

This paper cites Hyper- spherical prototype networks.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Hyper- spherical prototype networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.083153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 03bd326b-5581-456c-b43c-e39aef0a086d · outbound

This paper cites Automated flower classification over a large number of classes.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Automated flower classification over a large number of classes

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.067668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f1b51850-4474-4aa8-bd1c-c908da59ba80 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.051205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 24159cb1-0e1f-4e62-81af-10606aab1cba · outbound

This paper cites Federated Mutual Learning.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Federated Mutual Learning

Reference 30

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Source-reported events for the cited work

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Observation 9b891ea0-1f56-43e3-ab0f-66f290e74c81 · outbound

This paper cites Feduv: Uniformity and variance for heterogeneous federated learning.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Feduv: Uniformity and variance for heterogeneous federated learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.033106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 604baa65-bda8-4521-ac9d-dee98acb3831 · outbound

This paper cites Personalized federated learning with moreau envelopes.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Personalized federated learning with moreau envelopes

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:04.016776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation da6f9c76-0f13-4fb9-8a02-7931f086fec3 · outbound

This paper cites Hyperspherical consistency regularization.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Hyperspherical consistency regularization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.999546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4ba90e32-1365-4980-9352-b9b533b4bdc9 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 34

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Source-reported events for the cited work

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Observation c2a8a60d-42c0-4dbe-9116-065cf7f7c591 · outbound

This paper cites Fedproto: Federated proto- type learning across heterogeneous clients.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Fedproto: Federated proto- type learning across heterogeneous clients

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.983108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation abe8b4cb-84e8-4de7-8400-75b9bbecda6e · outbound

This paper cites Cosface: Large margin cosine loss for deep face recognition.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Cosface: Large margin cosine loss for deep face recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.966770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fd652b32-d792-4fd6-9bc5-6ee35ccd369c · outbound

This paper cites Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.949084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 90ad167b-bc4a-40c2-acd3-a83334082dc2 · outbound

This paper cites Fedgh: Heterogeneous federated learning with general- ized global header.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Fedgh: Heterogeneous federated learning with general- ized global header

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.931540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bd172fb6-2943-4ac8-bf1f-ac3b8c42eb3b · outbound

This paper cites Con- trolling update distance and enhancing fair trainable proto- types in federated learning under data and model heterogene- ity.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Con- trolling update distance and enhancing fair trainable proto- types in federated learning under data and model heterogene- ity

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.914580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 40155190-0cbf-4f78-aacc-3673f7ca9835 · outbound

This paper cites A survey on federated learning.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling A survey on federated learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.897029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9374efec-a440-451c-9ee7-c37d52310a4a · outbound

This paper cites Fedala: Adaptive local aggregation for personalized federated learning.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Fedala: Adaptive local aggregation for personalized federated learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.881585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 48f0d754-250f-438b-8749-971571f04591 · outbound

This paper cites Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in fed- erated learning.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in fed- erated learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.863712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a19633ab-b99c-421b-8256-dc81903e3c04 · outbound

This paper cites Deep mutual learning.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Deep mutual learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.844701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 65ec9312-feb9-4eb6-bd5e-3b8c17eb6352 · outbound

This paper cites Deep residual networks for hyperspectral image classi- fication.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Deep residual networks for hyperspectral image classi- fication

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.826295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cbad1c27-aa4b-41e1-a5f0-1ab6600f2feb · outbound

This paper cites Data-free knowledge distillation for heterogeneous federated learn- ing.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Data-free knowledge distillation for heterogeneous federated learn- ing

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.809186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cfb32289-c2cc-43ec-9103-5984b0b4f959 · outbound

This paper cites Resilient and communication efficient learning for heteroge- neous federated systems.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Resilient and communication efficient learning for heteroge- neous federated systems

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:55:03.789779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.