Paper Citation Record · LEDGER

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments

As of 14 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2507.04327.

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

pith.paper-citation-record.v1
2507.04327 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:58:08.042634Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:34:01.766196Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy28
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4045c70-4eb5-46ba-8bf7-2e44f5287ccb · outbound

This paper cites A closer look at memorization in deep networks.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments A closer look at memorization in deep networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:13.466468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5d440f1a-c9d8-4f5b-b24b-e82a7b8f3cab · outbound

This paper cites On Bridging Generic and Personalized Federated Learning for Image Classification.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments On Bridging Generic and Personalized Federated Learning for Image Classification

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:02.524288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b38c31c-bfd5-46bf-9856-a648ab3e6a2a · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:02.676986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 901d3977-9a0f-420f-a891-e2c76f7f4406 · outbound

This paper cites Learning both weights and connections for efficient neural network.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Learning both weights and connections for efficient neural network

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:13.273919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0bc48bfe-3640-46b9-b7e6-3b8d4c2f4507 · outbound

This paper cites Adaptive gradient sparsification for efficient federated learning: An online learning approach.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Adaptive gradient sparsification for efficient federated learning: An online learning approach

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:13.117541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation edc44092-28c0-4a53-9df2-8b9a18b18023 · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:03.078462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5c1fcc7-e6e2-4417-aeb5-336ab6be5b19 · outbound

This paper cites Model pruning enables efficient federated learning on edge devices.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Model pruning enables efficient federated learning on edge devices

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:12.893976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b821cb1b-46af-4192-bd22-20738002f114 · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Scaffold: Stochastic controlled averaging for federated learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:12.637286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a3136115-a661-43ed-9ef1-fbbdd2ddc028 · outbound

This paper cites Generalization in deep learning.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Generalization in deep learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:12.434851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d3696532-da5c-457d-bf64-97cd09a4cfb9 · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Learning multiple layers of features from tiny images

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:03.620208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ce182b3-8232-4b24-be9d-c94de59e2aa6 · outbound

This paper cites Tiny imagenet visual recognition challenge.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Tiny imagenet visual recognition challenge

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:12.284304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ebd229b-ba65-45e6-a435-78762dbdfb95 · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments FedMD: Heterogenous Federated Learning via Model Distillation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:03.877147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78a4d471-2467-4048-bcca-6410c45f09e8 · outbound

This paper cites Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:12.087417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b0cc8246-dfdf-465e-bdb2-229ae921c9ab · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Think Locally, Act Globally: Federated Learning with Local and Global Representations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:04.102767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb342a09-8ea3-4dfd-95d6-084fb8e79c03 · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Ensemble distillation for robust model fusion in federated learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:11.897390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d717e8d7-65e4-46d2-a40c-dc2188495c62 · outbound

This paper cites Dying ReLU and Initialization: Theory and Numerical Examples.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Dying ReLU and Initialization: Theory and Numerical Examples

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:04.270657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 829ca56c-eaae-427b-a421-8ad2931befdc · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architecture design.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Shufflenet v2: Practical guidelines for efficient cnn architecture design

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:04.468063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b451d9c-2482-4c2f-bbe5-fb3b8cc138bc · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Communication-efficient learning of deep networks from decentralized data

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:04.673717Z

Source-reported events for the cited work

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Observation 0fc3b891-44f5-4fe0-9eec-fd780e0ed7ce · outbound

This paper cites Federated learning for internet of things: A comprehensive survey.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Federated learning for internet of things: A comprehensive survey

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:11.712432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 683fd920-b804-4bf8-a429-62fc3c7ee4e1 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:04.996681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb6a18fd-c09e-40c3-80d7-e38dadbe3e5c · outbound

This paper cites Robust and communication-efficient federated learning from non-iid data.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Robust and communication-efficient federated learning from non-iid data

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:11.538679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5d864a07-255b-44c4-a23b-9a0acb88fbf5 · outbound

This paper cites Federated Mutual Learning.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Federated Mutual Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:05.327855Z

Source-reported events for the cited work

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Observation 543c51ca-132d-46b7-a1e0-0380b9f9c4d3 · outbound

This paper cites Sparsified sgd with memory.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Sparsified sgd with memory

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:11.337581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a2128a58-ac7f-4534-80ef-c2acf95a54cc · outbound

This paper cites Going deeper with convolutions.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Going deeper with convolutions

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:11.093312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation df3dffa6-554c-4400-9fa7-31134059fa3f · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Fedproto: Federated prototype learning across heterogeneous clients

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:10.901001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 504c5b3d-4b2c-484b-8168-48ef408e73a9 · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:05.874253Z

Source-reported events for the cited work

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Observation dc41ef2a-6b5c-44c8-8cc5-9f72379846dd · outbound

This paper cites Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:10.669895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9b4e727c-7e1b-41ce-ac9b-683cec4353ad · outbound

This paper cites Adaptive federated learning in resource constrained edge computing systems.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Adaptive federated learning in resource constrained edge computing systems

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:10.511700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8fea26e8-b2ae-4c65-a4fc-0412f3f4a546 · outbound

This paper cites Svdfed: Enabling communication-efficient federated learning via singular-value-decomposition.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Svdfed: Enabling communication-efficient federated learning via singular-value-decomposition

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:10.337255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4e7dba8-54ce-4287-b115-2202127864bb · outbound

This paper cites Why go full? elevating federated learning through partial network updates, 2024.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Why go full? elevating federated learning through partial network updates, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:10.116155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 914d7d3e-4ae8-4684-b9a3-fe609b05eaa7 · outbound

This paper cites Learning structured sparsity in deep neural networks.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Learning structured sparsity in deep neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:09.924163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3282917-d449-4674-92c8-395f556399e2 · outbound

This paper cites Communication-efficient federated learning via knowledge distillation.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Communication-efficient federated learning via knowledge distillation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:09.776984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2cb3fdb0-ccbb-4f60-912e-bc5376e3e6f4 · outbound

This paper cites Efficient federated learning on resource-constrained edge devices based on model pruning.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Efficient federated learning on resource-constrained edge devices based on model pruning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:09.590838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b72ecaf8-f28c-4ee1-bdb7-a55b0a8177ad · outbound

This paper cites Decoupling general and personalized knowledge in federated learning via additive and low-rank decomposition.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Decoupling general and personalized knowledge in federated learning via additive and low-rank decomposition

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:09.463497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b8c005eb-ca68-45a9-9bd8-507abe73ea2a · outbound

This paper cites Deep mutual learning.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Deep mutual learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:09.305216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 832b6537-f95b-4e1e-886b-5e3bb9b6779f · outbound

This paper cites A survey on federated learning.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments A survey on federated learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:09.117064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5c1533fb-a585-412b-8cf4-6ed9b1301f76 · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Understanding deep learning (still) requires rethinking generalization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:08.956480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4984c758-471c-4f4c-9dfd-1e33819bd3e4 · outbound

This paper cites FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:58:08.218163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b26abde1-92d7-40cf-a295-bbb59b16b9ed · outbound

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

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in federated learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:08.821914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dd3a3136-5241-4e3e-8b87-c2875a2a84d3 · outbound

This paper cites Deep residual networks for hyperspectral image classification.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Deep residual networks for hyperspectral image classification

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:08.669715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ebf3137c-04a2-4171-bd2e-c791c3bac591 · outbound

This paper cites Data-Free Knowledge Distillation for Heterogeneous Federated Learning.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:08.500797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 468c7c52-6981-43f1-9137-5f1279b08a0c · outbound

This paper cites write newline.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments write newline

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:08.042634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation cced5eb0-e204-4d87-b03b-54d4bfdf0bf3 · inbound

Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis cites this paper.

Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T16:34:01.766196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.