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Paper Citation Record · LEDGER

Sheaf-Based Federated Representation Learning

As of 15 August 2026, this Paper Citation Record lists 100 of 202 outbound references and 0 inbound Pith citation observations for arXiv:2608.10016.

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

pith.paper-citation-record.v1
2608.10016 v1

Coverage vector

measured 100 of 202 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:38:28.578015Z

measured 100 of 100 standing notices

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

100 of 202 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved98
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

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Outbound references

Observation da6be401-1097-4d66-9532-c85915b5e820 · outbound

This paper cites Absil, Robert Mahony, and Rodolphe Sepulchre.

Sheaf-Based Federated Representation Learning Absil, Robert Mahony, and Rodolphe Sepulchre

Reference 1

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Observation 86c8f7ef-f542-429e-aa7e-6478fb54ef48 · outbound

This paper cites Communication-efficient and robust multi-modal federated learning via latent-space consensus.

Sheaf-Based Federated Representation Learning Communication-efficient and robust multi-modal federated learning via latent-space consensus

Reference 2

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Observation aa476218-c0ef-45d3-b1dd-d4fc5836c1ad · outbound

This paper cites Bandeira, Amit Singer, and Daniel A.

Sheaf-Based Federated Representation Learning Bandeira, Amit Singer, and Daniel A

Reference 3

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Observation 337ddbe8-68da-4d8f-9731-b45b616e5e83 · outbound

This paper cites Semantic communications based on adaptive generative models and information bottleneck.

Sheaf-Based Federated Representation Learning Semantic communications based on adaptive generative models and information bottleneck

Reference 4

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Observation 221203f1-0c82-47c8-a22e-8e452076466f · outbound

This paper cites Sheaf neural networks with connection L aplacians.

Sheaf-Based Federated Representation Learning Sheaf neural networks with connection L aplacians

Reference 5

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Observation f9cc0a38-74ef-4351-b29c-69ebfa73deb4 · outbound

This paper cites First-order methods in optimization.

Sheaf-Based Federated Representation Learning First-order methods in optimization

Reference 6

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Observation 35740fa8-1e70-44df-b90d-8670ec949819 · outbound

This paper cites Bronstein.

Sheaf-Based Federated Representation Learning Bronstein

Reference 7

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Observation d8c58362-aff6-409a-af79-d41be41922d8 · outbound

This paper cites Sheaf theory, volume 170.

Sheaf-Based Federated Representation Learning Sheaf theory, volume 170

Reference 8

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Observation 3d91c47f-9f06-4a54-9a53-4d9fa2648a24 · outbound

This paper cites Multimodal federated learning: A survey.

Sheaf-Based Federated Representation Learning Multimodal federated learning: A survey

Reference 9

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Observation 1fbe6a9b-d8d6-4ffc-b230-ea3051d3ebbe · outbound

This paper cites Ranking and sparsifying a connection graph.

Sheaf-Based Federated Representation Learning Ranking and sparsifying a connection graph

Reference 10

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Observation 7a71f8ee-1cfc-48f4-8a57-0a94d2efa981 · outbound

This paper cites Exploiting shared representations for personalized federated learning.

Sheaf-Based Federated Representation Learning Exploiting shared representations for personalized federated learning

Reference 11

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Observation a65a3f6b-71f1-44ed-a974-56528d39a1c6 · outbound

This paper cites Sheaves, cosheaves and applications.

Sheaf-Based Federated Representation Learning Sheaves, cosheaves and applications

Reference 12

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Observation 19526cd7-419d-4f7c-83de-65cd1ee9a2ed · outbound

This paper cites Causal abstraction learning based on the semantic embedding principle.

Sheaf-Based Federated Representation Learning Causal abstraction learning based on the semantic embedding principle

Reference 13

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Observation ac6fb9ab-5c31-4fbf-b5c2-9ccb07d329e7 · outbound

This paper cites Learning sheaf L aplacian optimizing restriction maps.

Sheaf-Based Federated Representation Learning Learning sheaf L aplacian optimizing restriction maps

Reference 15

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Observation caea8c8a-8b23-4717-8136-70c25b24e280 · outbound

This paper cites Learning the structure of connection graphs.

Sheaf-Based Federated Representation Learning Learning the structure of connection graphs

Reference 16

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Observation 437349e9-fb22-42f6-9545-d7e4da8f2e1a · outbound

This paper cites HeteroFL : Computation and communication efficient federated learning for heterogeneous clients.

Sheaf-Based Federated Representation Learning HeteroFL : Computation and communication efficient federated learning for heterogeneous clients

Reference 17

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Observation ca60bc5d-eb3e-4851-875f-e2f6fd1ab039 · outbound

This paper cites A new look and convergence rate of federated multitask learning with L aplacian regularization.

Sheaf-Based Federated Representation Learning A new look and convergence rate of federated multitask learning with L aplacian regularization

Reference 18

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Observation e1ccb8ae-6916-4421-99c0-2dd2355c502a · outbound

This paper cites Federated contrastive learning for decentralized unlabeled medical images.

Sheaf-Based Federated Representation Learning Federated contrastive learning for decentralized unlabeled medical images

Reference 19

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Observation d5251c20-e926-4ce2-993f-ed03cb574510 · outbound

This paper cites Arias, and Steven T.

Sheaf-Based Federated Representation Learning Arias, and Steven T

Reference 20

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Observation d0e9198e-2ca0-4b95-b487-a1de9776600e · outbound

This paper cites Regularized multi--task learning.

Sheaf-Based Federated Representation Learning Regularized multi--task learning

Reference 21

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Observation b336a8cc-f00d-4368-93e7-dce8e4b2823a · outbound

This paper cites Learning multiple tasks with kernel methods.

Sheaf-Based Federated Representation Learning Learning multiple tasks with kernel methods

Reference 22

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Observation 5ec0fb82-a373-4791-8634-b10175751446 · outbound

This paper cites Dynamic relative representations for goal-oriented semantic communications.

Sheaf-Based Federated Representation Learning Dynamic relative representations for goal-oriented semantic communications

Reference 23

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Observation edfb46f1-0451-41e3-bee7-f7b542e31dda · outbound

This paper cites Frame-based zero-shot semantic channel equalization for AI -native communications.

Sheaf-Based Federated Representation Learning Frame-based zero-shot semantic channel equalization for AI -native communications

Reference 24

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Observation 991295a9-ebc4-4d43-ad17-9ae6b66144da · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures.

Sheaf-Based Federated Representation Learning Model inversion attacks that exploit confidence information and basic countermeasures

Reference 25

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Observation c153e114-0de7-4ead-a8f1-6e0db1f87f3e · outbound

This paper cites Sheaf A lign: A sheaf-theoretic framework for decentralized multimodal alignment.

Sheaf-Based Federated Representation Learning Sheaf A lign: A sheaf-theoretic framework for decentralized multimodal alignment

Reference 26

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Observation 250bb8cb-33da-46da-b1e5-5eb70c273ac9 · outbound

This paper cites Learning network sheaves for AI -native semantic communication.

Sheaf-Based Federated Representation Learning Learning network sheaves for AI -native semantic communication

Reference 27

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Observation 5c2a1d68-361c-4cb9-aa42-cb7b52590525 · outbound

This paper cites Beyond transmitting bits: Context, semantics, and task-oriented communications.

Sheaf-Based Federated Representation Learning Beyond transmitting bits: Context, semantics, and task-oriented communications

Reference 28

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Observation 2a1130b2-ff13-4e89-aeaa-69512090a570 · outbound

This paper cites Fedx: Unsupervised federated learning with cross knowledge distillation.

Sheaf-Based Federated Representation Learning Fedx: Unsupervised federated learning with cross knowledge distillation

Reference 29

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Observation 6ca7bdb2-ac91-45dd-8c3b-509d765ed34e · outbound

This paper cites Toward a spectral theory of cellular sheaves.

Sheaf-Based Federated Representation Learning Toward a spectral theory of cellular sheaves

Reference 30

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Observation 49b464bc-d978-49b3-8b53-933a07efdfb3 · outbound

This paper cites u ttebr \.

Sheaf-Based Federated Representation Learning u ttebr \

Reference 31

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Observation f51ae61d-e143-4e29-9826-7920fa87b77c · outbound

This paper cites Tackling feature and sample heterogeneity in decentralized multi-task learning: A sheaf-theoretic approach.

Sheaf-Based Federated Representation Learning Tackling feature and sample heterogeneity in decentralized multi-task learning: A sheaf-theoretic approach

Reference 32

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Observation 37f7bb41-bca5-4689-9269-652cbe61fb14 · outbound

This paper cites Clustered multi-task learning: A convex formulation.

Sheaf-Based Federated Representation Learning Clustered multi-task learning: A convex formulation

Reference 33

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Observation 63e93283-dca4-4aa5-ba25-cfe7ffe1b782 · outbound

This paper cites Communication-efficient distributed dual coordinate ascent.

Sheaf-Based Federated Representation Learning Communication-efficient distributed dual coordinate ascent

Reference 34

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Observation ccc13243-0b7d-474a-b9f7-0e0d32ddcf7d · outbound

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Sheaf-Based Federated Representation Learning Mcmahan, et al

Reference 35

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Observation b8f3ce81-828a-4a94-a383-1ee496d6af1d · outbound

This paper cites SCAFFOLD : Stochastic controlled averaging for federated learning.

Sheaf-Based Federated Representation Learning SCAFFOLD : Stochastic controlled averaging for federated learning

Reference 36

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Observation 12494ef2-87cd-40aa-ae28-dbacd046dc36 · outbound

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Sheaf-Based Federated Representation Learning Optimal whitening and decorrelation

Reference 37

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Observation fd626a06-2f10-479a-a8d6-4443ddb4a365 · outbound

This paper cites LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets.

Sheaf-Based Federated Representation Learning LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

Reference 38

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Observation b5d32e35-25a4-474c-8027-2cca7345e4a2 · outbound

This paper cites Federated Learning on Riemannian Manifolds.

Sheaf-Based Federated Representation Learning Federated Learning on Riemannian Manifolds

Reference 39

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Observation 0fd6fa42-ce12-40fd-b8ee-211b304acab3 · outbound

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Sheaf-Based Federated Representation Learning Model-contrastive federated learning

Reference 40

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Observation a54fa529-053d-41fe-b497-b2159ce41d65 · outbound

This paper cites Federated optimization in heterogeneous networks.

Sheaf-Based Federated Representation Learning Federated optimization in heterogeneous networks

Reference 41

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Observation 8a04f1c8-a548-497c-9ba5-39d5657fd3d2 · outbound

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

Sheaf-Based Federated Representation Learning Think Locally, Act Globally: Federated Learning with Local and Global Representations

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Observation 074ced25-9aa6-41da-b6ac-619fb4814794 · outbound

This paper cites Adding vs.

Sheaf-Based Federated Representation Learning Adding vs

Reference 43

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Observation 06df3dc9-d998-4a9a-ada3-19527b40d7b8 · outbound

This paper cites Personalized federated learning via feature distribution adaptation.

Sheaf-Based Federated Representation Learning Personalized federated learning via feature distribution adaptation

Reference 44

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source=arxiv_source observed=2026-08-12T00:38:26.369666Z digest=sha256:5bf33fa458f92dc9e5debc332fa4cbd409c5fd4344d147db9f837610f33d7632

Observation 3c980c91-82d0-4a13-93d7-d410b99ba2f7 · outbound

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

Sheaf-Based Federated Representation Learning Communication-efficient learning of deep networks from decentralized data

Reference 45

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source=arxiv_source observed=2026-08-12T00:38:26.407027Z digest=sha256:449bd2f44b5631fa9b2cda6720e38370047f12c00a282d2192ad8c22e03ec189

Observation 8a0ed6ce-b929-4339-b968-845c2527ac39 · outbound

This paper cites Exploiting unintended feature leakage in collaborative learning.

Sheaf-Based Federated Representation Learning Exploiting unintended feature leakage in collaborative learning

Reference 46

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source=arxiv_source observed=2026-08-12T00:38:26.520291Z digest=sha256:606855b2581214224818d968ad3dff5deb56572830ca0d33f75dd248b463d1e6

Observation 940febb3-16ea-499c-82e4-4aca92156f63 · outbound

This paper cites Contrastive and non-contrastive strategies for federated self-supervised representation learning and deep clustering.

Sheaf-Based Federated Representation Learning Contrastive and non-contrastive strategies for federated self-supervised representation learning and deep clustering

Reference 47

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Observation 1f3f68e2-74ea-429e-b3e1-3759663360eb · outbound

This paper cites Relative representations enable zero-shot latent space communication.

Sheaf-Based Federated Representation Learning Relative representations enable zero-shot latent space communication

Reference 48

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source=arxiv_source observed=2026-08-12T00:38:26.594756Z digest=sha256:837ec38d4b21cadfd93ccfab567c358929dfb66780eb81fa40176e0fef128ff3

Observation 5d955ea4-8cff-48e1-a45f-2c2f1ea15206 · outbound

This paper cites Introductory Lectures on Convex Optimization: A Basic Course, volume 87 of Applied Optimization.

Sheaf-Based Federated Representation Learning Introductory Lectures on Convex Optimization: A Basic Course, volume 87 of Applied Optimization

Reference 49

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Observation f459255a-2690-4eff-94ae-8cea25d90e6c · outbound

This paper cites Latent space alignment for AI -native mimo semantic communications.

Sheaf-Based Federated Representation Learning Latent space alignment for AI -native mimo semantic communications

Reference 50

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source=arxiv_source observed=2026-08-12T00:38:26.708001Z digest=sha256:f4ec98bee728d75e8e90194e66f10c960e3f665553be93cc1f0a0acb1b214ce6

Observation 6ba2f52e-fc60-4bbd-83fb-7d3cde4d0b88 · outbound

This paper cites SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations.

Sheaf-Based Federated Representation Learning SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations

Reference 51

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source=arxiv_source observed=2026-08-12T00:38:26.801095Z digest=sha256:671051ae6dd60e946472b87bfd1da05feb9e7621df59dfeaada753ce622fd2bd

Observation 000743a4-83ae-4dcb-8cc9-77a815076d4d · outbound

This paper cites A convergence theorem for non negative almost supermartingales and some applications.

Sheaf-Based Federated Representation Learning A convergence theorem for non negative almost supermartingales and some applications

Reference 52

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source=arxiv_source observed=2026-08-12T00:38:26.864283Z digest=sha256:4499d810fcdb084cb3d4ab30be4e6b4f86b1b2e4efde71a7eb1613bd442a2a6e

Observation 9d172392-a6f9-4e95-ba47-45b4668bc4d3 · outbound

This paper cites A framework for parallel and distributed training of neural networks.

Sheaf-Based Federated Representation Learning A framework for parallel and distributed training of neural networks

Reference 53

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source=arxiv_source observed=2026-08-12T00:38:26.906468Z digest=sha256:0946c67f21790d0f53ab2ac03dc1c7c44434cd3d384a61e4668540e4879ca6dd

Observation ac8eaf8a-ecea-4fa7-b7df-0359bf7ccde3 · outbound

This paper cites Sch \"o nemann.

Sheaf-Based Federated Representation Learning Sch \"o nemann

Reference 54

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source=arxiv_source observed=2026-08-12T00:38:26.912481Z digest=sha256:fddc5bab5947e57a2f2791641966f73235dde346478496b25e0f931d2d1ae810

Observation 0d4d6192-23be-4b86-8b5f-1fa42ef15156 · outbound

This paper cites Khalil, and Hongliang Li.

Sheaf-Based Federated Representation Learning Khalil, and Hongliang Li

Reference 55

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source=arxiv_source observed=2026-08-12T00:38:26.918291Z digest=sha256:58cd3d71ef30fae6198198a6c57a1534c2a0fd8ac73163e8796b8d3dc467a5e5

Observation 7aa818e0-9087-4c05-a7b7-06ff1b79b4c4 · outbound

This paper cites Membership inference attacks against machine learning models.

Sheaf-Based Federated Representation Learning Membership inference attacks against machine learning models

Reference 56

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source=arxiv_source observed=2026-08-12T00:38:26.924366Z digest=sha256:7a914dd7e0c55f9cc7889d0154142f499e4e7c84a8dd464950f0b306499acc28

Observation 52092997-c1bd-47ec-8114-f439d5dfd349 · outbound

This paper cites Angular synchronization by eigenvectors and semidefinite programming.

Sheaf-Based Federated Representation Learning Angular synchronization by eigenvectors and semidefinite programming

Reference 57

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source=arxiv_source observed=2026-08-12T00:38:26.929916Z digest=sha256:d753f775c031ae13523b545200c932739ccfe39e56ee52cbccf27cf4a9710820

Observation 6a0f9588-c086-4b46-bcd7-28ebaf4869ce · outbound

This paper cites Vector diffusion maps and the connection L aplacian.

Sheaf-Based Federated Representation Learning Vector diffusion maps and the connection L aplacian

Reference 58

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source=arxiv_source observed=2026-08-12T00:38:26.977109Z digest=sha256:97de93cb9dce575514f50c45da47bdc84517bedc2fd19f3e1d740bf5af538c00

Observation 4cf5b7cf-0f59-43b5-af1e-35162a044357 · outbound

This paper cites Federated multi-task learning.

Sheaf-Based Federated Representation Learning Federated multi-task learning

Reference 59

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source=arxiv_source observed=2026-08-12T00:38:27.062778Z digest=sha256:0d79a37f4ae378af0d4962c6e272de8ba99a0cdcd7ed9b5b83e6b5f9d886e187

Observation b1b9b5db-f46f-448c-9c94-e800610e1f85 · outbound

This paper cites Goal-oriented and semantic communication in 6G AI -native networks: The 6G-GOALS approach.

Sheaf-Based Federated Representation Learning Goal-oriented and semantic communication in 6G AI -native networks: The 6G-GOALS approach

Reference 60

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source=arxiv_source observed=2026-08-12T00:38:27.153993Z digest=sha256:3cfc946be562f001d136230244bfb7c46ef1f04912eab52b76d4cba091685c83

Observation 940c8b86-b7dc-4be4-9d47-cade623aaaa8 · outbound

This paper cites Fedproto: Federated prototype learning across heterogeneous clients.

Sheaf-Based Federated Representation Learning Fedproto: Federated prototype learning across heterogeneous clients

Reference 62

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source=arxiv_source observed=2026-08-12T00:38:27.190661Z digest=sha256:f6ecf2c8c5ee04cddf0fbb95a5d705be7aa2c22574f7a2cc95c934cd5243ba40

Observation 4c7ffe35-88e5-45e4-9b75-cd2fbe7e221c · outbound

This paper cites Distributed methods for synchronization of orthogonal matrices over graphs.

Sheaf-Based Federated Representation Learning Distributed methods for synchronization of orthogonal matrices over graphs

Reference 63

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source=arxiv_source observed=2026-08-12T00:38:27.196131Z digest=sha256:d13118a22832b6fcbc6468e96b5e4a53f574e998ad94143f94d4d13c6557a77d

Observation 860750ed-8189-49e6-b945-19bd73db210f · outbound

This paper cites Fedntproto: A prototype-based approach for personalized federated learning.

Sheaf-Based Federated Representation Learning Fedntproto: A prototype-based approach for personalized federated learning

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Observation d1cda90c-0251-4fd9-9f3a-b0c0fed27798 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere.

Sheaf-Based Federated Representation Learning Understanding contrastive representation learning through alignment and uniformity on the hypersphere

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source=arxiv_source observed=2026-08-12T00:38:27.244079Z digest=sha256:abef2591e946ab00bbc4a45805bfa50f2ecf706c1fcb134f8a2b4d2902b006cc

Observation 526c0674-6270-4d45-b609-b1937db61a8c · outbound

This paper cites Federated unsupervised representation learning.

Sheaf-Based Federated Representation Learning Federated unsupervised representation learning

Reference 66

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source=arxiv_source observed=2026-08-12T00:38:27.325723Z digest=sha256:1adb2f4165fb76e08b849870d8213d051e5e7d61cf553b5d7fb995caecb0507e

Observation 6bc3a227-76da-4316-906e-dcb24318deeb · outbound

This paper cites Stochastic whitening batch normalization.

Sheaf-Based Federated Representation Learning Stochastic whitening batch normalization

Reference 67

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source=arxiv_source observed=2026-08-12T00:38:27.402655Z digest=sha256:156fac5d6d169758168c6a4eb73b71a2af60aa20b5f932481c473daef59d90bd

Observation c1436bd4-eb84-4922-a441-fa77a5a5d6f7 · outbound

This paper cites Learning sparse task relations in multi-task learning.

Sheaf-Based Federated Representation Learning Learning sparse task relations in multi-task learning

Reference 68

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source=arxiv_source observed=2026-08-12T00:38:27.427878Z digest=sha256:90e17d9ec688aebbcebb40b508d70016d41bf86969426f7939d5f99c7de7ce9e

Observation 64697010-11c1-4f4b-b508-d88a70c34c54 · outbound

This paper cites A convex formulation for learning task relationships in multi-task learning.

Sheaf-Based Federated Representation Learning A convex formulation for learning task relationships in multi-task learning

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source=arxiv_source observed=2026-08-12T00:38:27.435271Z digest=sha256:105feadd32d9d888988de3e6a438775308fbd3fbdd7c9f6a7ef7424952a20cc3

Observation 0bb7fefe-a489-4723-aba6-bf807b226db0 · outbound

This paper cites Collaborative unsupervised visual representation learning from decentralized data.

Sheaf-Based Federated Representation Learning Collaborative unsupervised visual representation learning from decentralized data

Reference 70

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source=arxiv_source observed=2026-08-12T00:38:27.441326Z digest=sha256:64e1d73d150242f26dada907ea477a7eda77fe4877116e2442b773a3ad9bec86

Observation 8b3835b4-a61b-420d-8e5c-15067fd26230 · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data , booktitle =.

Sheaf-Based Federated Representation Learning Communication-Efficient Learning of Deep Networks from Decentralized Data , booktitle =

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source=arxiv_source observed=2026-08-12T00:38:27.447362Z digest=sha256:e577b006f27a9c34a44de2e6325b8939816e933a489190ad90e940beea8fc071

Observation db184444-5f1a-4968-a926-1cb5daee5248 · outbound

This paper cites IEEE Communications Magazine , volume=.

Sheaf-Based Federated Representation Learning IEEE Communications Magazine , volume=

Reference 72

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Observation 5281675c-4d93-45c9-83e4-5b82f7d6cb9b · outbound

This paper cites Proceedings of the Third Conference on Machine Learning and Systems (MLSys) , year =.

Sheaf-Based Federated Representation Learning Proceedings of the Third Conference on Machine Learning and Systems (MLSys) , year =

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source=arxiv_source observed=2026-08-12T00:38:27.555955Z digest=sha256:6d528b03827611e79ea83637b07c04ff30028826dfd09b5c6b5e09d1b68d484b

Observation 13261314-c967-41b9-a61a-1313ce4bb203 · outbound

This paper cites Optimizing Methods in Statistics , pages=.

Sheaf-Based Federated Representation Learning Optimizing Methods in Statistics , pages=

Reference 74

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source=arxiv_source observed=2026-08-12T00:38:27.684807Z digest=sha256:031401334bf81ad7f4a70e9f0a4c0a862e4fbf5debf5df363c791f5750ff9ad6

Observation 23fbed42-d71d-464e-a528-71c3cd065f2a · outbound

This paper cites ICML , year=.

Sheaf-Based Federated Representation Learning ICML , year=

Reference 75

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source=arxiv_source observed=2026-08-12T00:38:27.696780Z digest=sha256:51042b745a4816af4d4cd47eaa06d92b1cb1b0a1dae8f821558a612cdfefe05d

Observation 4e878c56-1771-44cb-a012-00c5c10c83f8 · outbound

This paper cites CVPR , year=.

Sheaf-Based Federated Representation Learning CVPR , year=

Reference 76

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source=arxiv_source observed=2026-08-12T00:38:27.704045Z digest=sha256:38ba2b097b9d38dbef9b809687a86e70992b95b58027b6dc5a03d1ff0de7e2f8

Observation 4eb16cef-cf6b-4284-9db4-3ab49d1a77cb · outbound

This paper cites CVPR , year=.

Sheaf-Based Federated Representation Learning CVPR , year=

Reference 77

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source=arxiv_source observed=2026-08-12T00:38:27.709570Z digest=sha256:3238f93bc87f0bbc438f6c456a1d13b503f9dc62c38b0609e9d800fc4ba76eca

Observation 225a6d00-9734-4dd1-bc37-e9c7db93b140 · outbound

This paper cites ICML , year=.

Sheaf-Based Federated Representation Learning ICML , year=

Reference 78

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source=arxiv_source observed=2026-08-12T00:38:27.751753Z digest=sha256:8a5bc9b2c6070c601fc69b7450c5bc45a118d90f44227e18b289a8699f154c54

Observation a69e3df1-63cd-429f-b954-6079870656f7 · outbound

This paper cites 2020 , series =.

Sheaf-Based Federated Representation Learning 2020 , series =

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source=arxiv_source observed=2026-08-12T00:38:27.848517Z digest=sha256:d7d74f3426e9fa6f1ca8fd0e6bacaa993e54e88a81f153bc23f73b3aecb55c18

Observation faeb3081-f97f-417f-b2ac-426078014093 · outbound

This paper cites Learning Sheaf.

Sheaf-Based Federated Representation Learning Learning Sheaf

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source=arxiv_source observed=2026-08-12T00:38:27.912663Z digest=sha256:0aae3e2295d96ed9c8b992eb35e2363f30e67a56dd915eb2735c50d7a545216f

Observation db932e30-9d97-4f30-bb6f-7bf10380c47b · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

Sheaf-Based Federated Representation Learning Flamingo: a Visual Language Model for Few-Shot Learning

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source=arxiv_source observed=2026-08-12T00:38:27.930263Z digest=sha256:f495010c72c250e0d9c2c8e00b29a672a289ab5ccd0094f3724cbb7a399361be

Observation f3970ccd-6b43-47fb-9a4c-067b267af62f · outbound

This paper cites an unresolved cited work.

Sheaf-Based Federated Representation Learning Unresolved cited work

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source=arxiv_source observed=2026-08-12T00:38:27.937702Z digest=sha256:9ca07e8f3a2672af903167835cc5c1b89ee92e9525108545cf51efcafcef5b94

Observation 5ee8b6fd-ef69-4f23-bb9c-b09ef35fff2c · outbound

This paper cites and others , URL =.

Sheaf-Based Federated Representation Learning and others , URL =

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source=arxiv_source observed=2026-08-12T00:38:27.943103Z digest=sha256:0ff0d69524bdd110529d983e82f4ba48499fcd79a9e217501dfa076ade950d93

Observation 7a47d0cd-ee32-4605-90bb-aae424e6e96a · outbound

This paper cites International Conference on Machine Learning , pages=.

Sheaf-Based Federated Representation Learning International Conference on Machine Learning , pages=

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source=arxiv_source observed=2026-08-12T00:38:27.948339Z digest=sha256:da935e0bc9d69acccee7d0b8981148b1c6fb39105c1ec4f532738a0690d130cf

Observation 8fc954f1-3ee7-439f-867f-2ace055b78bc · outbound

This paper cites Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS) , year=.

Sheaf-Based Federated Representation Learning Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS) , year=

Reference 85

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source=arxiv_source observed=2026-08-12T00:38:27.960358Z digest=sha256:674425633681cd87fef273a3faee88647304c69b9238f95890c4bde86dcdb9de

Observation 75ad05e9-4060-4158-8f85-c3ff73d2cfe6 · outbound

This paper cites IEEE Symposium on Security and Privacy (S&P) , pages=.

Sheaf-Based Federated Representation Learning IEEE Symposium on Security and Privacy (S&P) , pages=

Reference 86

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no resolver link, observed 2026-08-12T00:38:28.043167Z

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source=arxiv_source observed=2026-08-12T00:38:28.043167Z digest=sha256:537bbf498edc46c89fe1c1fe3c3ea947ecb90797b24381f63b4d70991e7688a3

Observation 7ee55bd5-5919-431c-99a7-b8c533e34c9c · outbound

This paper cites IEEE Symposium on Security and Privacy (S&P) , year=.

Sheaf-Based Federated Representation Learning IEEE Symposium on Security and Privacy (S&P) , year=

Reference 87

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-12T00:38:28.132516Z digest=sha256:b2d6bb979b757e4a5ac6007a9f9e4511d23ad3220504fcb5c2a85fb6d83a72a3

Observation e30e2a0a-f51a-44fd-abee-12169d6178b2 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=.

Sheaf-Based Federated Representation Learning Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year=

Reference 88

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no resolver link, observed 2026-08-12T00:38:28.261597Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-12T00:38:28.261597Z digest=sha256:943c40e1e7e155007f62f029bbed47fb042492e5f6776121c36a73f86e2a5509

Observation 1bfb0763-ffe9-4fce-a176-2b3a35ba028f · outbound

This paper cites 1997 , publisher=.

Sheaf-Based Federated Representation Learning 1997 , publisher=

Reference 89

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-12T00:38:28.289598Z digest=sha256:e2858eb912dcf8e039e6555b2d25ac3dfa62674f5bdcf0403341bb0346cc17aa

Observation a0227427-5090-451e-926c-2a3ae826cdb7 · outbound

This paper cites ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

Sheaf-Based Federated Representation Learning ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 90

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-12T00:38:28.295487Z digest=sha256:cb1d5c35e3d38d7c7f5f1bef90df624d17cbbd5f2ea3b00eddd604041923b852

Observation d6c03773-7770-46c4-8bdb-5ef8b41dabf4 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =.

Sheaf-Based Federated Representation Learning Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =

Reference 91

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no resolver link, observed 2026-08-12T00:38:28.301207Z

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source=arxiv_source observed=2026-08-12T00:38:28.301207Z digest=sha256:fb3fe4c0ddac7fa848b3dec98a0678730dabee4a26eefa45c67aaa08e0997058

Observation ac482ae1-3166-4f7c-866d-fb5f86495868 · outbound

This paper cites International Conference on Medical Image Computing and Computer-Assisted Intervention , pages=.

Sheaf-Based Federated Representation Learning International Conference on Medical Image Computing and Computer-Assisted Intervention , pages=

Reference 92

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source=arxiv_source observed=2026-08-12T00:38:28.306435Z digest=sha256:27b7bbf4817d7d2f2b7578c419fa901d77b4e7892adc25575d977a9809686d18

Observation 6672debe-8065-40a1-99bf-1e84ea8c9e8c · outbound

This paper cites Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI) , pages =.

Sheaf-Based Federated Representation Learning Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI) , pages =

Reference 93

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no resolver link, observed 2026-08-12T00:38:28.312683Z

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source=arxiv_source observed=2026-08-12T00:38:28.312683Z digest=sha256:7150d0e1ac6247c53d41b7ed90e8855195272decb239d18fa60f3cc5731db34d

Observation 36593656-778d-4548-9167-50b6b4bf8c9a · outbound

This paper cites Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in.

Sheaf-Based Federated Representation Learning Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in

Reference 94

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source=arxiv_source observed=2026-08-12T00:38:28.327432Z digest=sha256:72edf5ece07dd25bd1f51b3305300ff59036ecff373944efdec5d563e4ab0005

Observation ba088499-8b50-491e-8473-85f14eb82570 · outbound

This paper cites Journal of Applied and Computational Topology , year =.

Sheaf-Based Federated Representation Learning Journal of Applied and Computational Topology , year =

Reference 95

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-12T00:38:28.444578Z digest=sha256:68434690d81ce6459982ccabc984fdba87492fb151e8facad850b088d237a0f7

Observation 7cd9eaf6-0c89-47a7-9b71-afc621a4c207 · outbound

This paper cites an unresolved cited work.

Sheaf-Based Federated Representation Learning Unresolved cited work

Reference 96

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source=arxiv_source observed=2026-08-12T00:38:28.508787Z digest=sha256:dd5a31dd5b55fac956ad3c2320589079ed2bfd984068b9352ad67ca0e7211bcf

Observation 73835bed-9f2e-47ee-a340-9e55ca976622 · outbound

This paper cites Applied and Computational Harmonic Analysis , volume=.

Sheaf-Based Federated Representation Learning Applied and Computational Harmonic Analysis , volume=

Reference 97

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source=arxiv_source observed=2026-08-12T00:38:28.537642Z digest=sha256:7e95a6818f2dc0776356f443eb7d72de40bcc1c2c7107d64be958506ba98650d

Observation e26ad436-bc5e-4af9-81e4-b1dbefc7819c · outbound

This paper cites Vector Diffusion Maps and the Connection.

Sheaf-Based Federated Representation Learning Vector Diffusion Maps and the Connection

Reference 98

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source=arxiv_source observed=2026-08-12T00:38:28.547191Z digest=sha256:9efea3013d3473265c175b13ee221c2249122dace91d67de8ae6264a31bbf1c1

Observation fda1d7b3-90c4-484f-9309-ce35c1adfb67 · outbound

This paper cites Internet Mathematics , volume=.

Sheaf-Based Federated Representation Learning Internet Mathematics , volume=

Reference 99

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source=arxiv_source observed=2026-08-12T00:38:28.561111Z digest=sha256:c0bc1b7a1d9a3bf9c0835e497e1425ee08206fc77b85cf7db2f205ada77dd1c0

Observation eb67719d-297f-4e5a-a78d-9cd2d79c129d · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

Sheaf-Based Federated Representation Learning Forty-second International Conference on Machine Learning , year=

Reference 100

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source=arxiv_source observed=2026-08-12T00:38:28.566913Z digest=sha256:42588add910ebee706da8177c258fd671eed1d0e9752d486faa5535d4d6f06f0

Observation f3a9a294-8786-4ca1-9bb6-4b5fad566f8f · outbound

This paper cites 2004 , series =.

Sheaf-Based Federated Representation Learning 2004 , series =

Reference 101

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source=arxiv_source observed=2026-08-12T00:38:28.572260Z digest=sha256:34570f38604fb4763c25b9a43295a2f3195760782937a936c203312566a65221

Observation 51b61a85-2dc2-4207-9cb1-fcab01bd25fa · outbound

This paper cites 2017 , publisher=.

Sheaf-Based Federated Representation Learning 2017 , publisher=

Reference 102

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source=arxiv_source observed=2026-08-12T00:38:28.578015Z digest=sha256:9c01cfcbd17c02f8ebf795385b62d52d41550c409a5d5a7ee897c7d3a34f0b5f

Pith citing papers

No inbound Pith citation observations are available.