Paper Citation Record · LEDGER

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks

As of 9 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2507.22339.

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

pith.paper-citation-record.v1
2507.22339 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:56:02.545058Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:13:00.027112Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T19:13:02.169334Z

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy38
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 49db320b-4248-453a-9584-924dc9ecbe9d · outbound

This paper cites Revolutionizing future connectivity: A contem- porary survey on AI-empowered satellite-based non-terrestrial networks in 6G,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Revolutionizing future connectivity: A contem- porary survey on AI-empowered satellite-based non-terrestrial networks in 6G,

Reference 1

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Observation 9c24069d-e56c-49d7-9c88-15554b432859 · outbound

This paper cites A survey of next-generation computing technologies in space-air-ground integrated networks,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks A survey of next-generation computing technologies in space-air-ground integrated networks,

Reference 2

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Observation 3bf42af0-0e04-4fdb-9ad5-ee597197f8cb · outbound

This paper cites Energy-efficient computation peer offloading in satellite edge computing networks,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Energy-efficient computation peer offloading in satellite edge computing networks,

Reference 3

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Observation 832f6f59-0b04-404f-9d5a-bad10480c5d0 · outbound

This paper cites Satellite edge intelligence: DRL-based resource management for task inference in LEO-based satellite-ground collaborative networks,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Satellite edge intelligence: DRL-based resource management for task inference in LEO-based satellite-ground collaborative networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.992675Z

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Observation 6fefd745-d19c-4270-b34c-e0587a014af0 · outbound

This paper cites Satellite internet of things for smart agriculture applications: A case study of computer vision,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Satellite internet of things for smart agriculture applications: A case study of computer vision,

Reference 5

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Observation 15df7e9d-f339-4324-b1ae-29a3dfa270bc · outbound

This paper cites Semi-supervised federated learning for assessing building damage from satellite imagery,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Semi-supervised federated learning for assessing building damage from satellite imagery,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.973579Z

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Observation b3d3786a-7744-4643-a489-7d9f4990fdb3 · outbound

This paper cites APT- SAT: An adaptive DNN partitioning and task offloading framework within collaborative satellite computing environments,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks APT- SAT: An adaptive DNN partitioning and task offloading framework within collaborative satellite computing environments,

Reference 7

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Observation 876b6d40-12c6-46d7-959d-25a6a52cc137 · outbound

This paper cites Adaptive configuration for heterogeneous participants in decentralized federated learning,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Adaptive configuration for heterogeneous participants in decentralized federated learning,

Reference 8

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Observation db4ac9c2-8c30-4251-a425-2584e3af45fc · outbound

This paper cites Resource management for MEC assisted multi-layer federated learning framework,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Resource management for MEC assisted multi-layer federated learning framework,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.945990Z

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Observation 027408bb-21d4-42e8-8361-6ad0b8d9ee42 · outbound

This paper cites Communication-efficient satellite-ground federated learning through progressive weight quantization,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Communication-efficient satellite-ground federated learning through progressive weight quantization,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.936915Z

Source-reported events for the cited work

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Observation 656d267e-a47a-4c35-a154-8b6d7f812186 · outbound

This paper cites FedSN: A fed- erated learning framework over heterogeneous LEO satellite networks,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks FedSN: A fed- erated learning framework over heterogeneous LEO satellite networks,

Reference 11

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Observation 8e435ce6-591f-4ff8-bc3d-81e55c09ff48 · outbound

This paper cites ALANINE: A novel decentralized personalized federated learning for heterogeneous leo satellite constellation,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks ALANINE: A novel decentralized personalized federated learning for heterogeneous leo satellite constellation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.922760Z

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Observation d4f3f153-177c-462e-9c10-18cb3f95674a · outbound

This paper cites Federated learning on non-IID data silos: An experimental study,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Federated learning on non-IID data silos: An experimental study,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.914229Z

Source-reported events for the cited work

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Observation 3d69a386-81a9-4b73-a31f-bed81e0cf523 · outbound

This paper cites Self-supervised spatio-temporal representation learning of satellite image time series,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Self-supervised spatio-temporal representation learning of satellite image time series,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.905680Z

Source-reported events for the cited work

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Observation 4ce81dc0-807c-430f-94b6-f97727c87b16 · outbound

This paper cites Energy-efficient federated learning for earth observation in LEO satellite systems,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Energy-efficient federated learning for earth observation in LEO satellite systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.896727Z

Source-reported events for the cited work

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Observation 50c51235-6a2d-4681-842b-298617830545 · outbound

This paper cites Connection- density-aware satellite-ground federated learning via asynchronous dy- namic aggregation,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Connection- density-aware satellite-ground federated learning via asynchronous dy- namic aggregation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.887916Z

Source-reported events for the cited work

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Observation 24bcd589-2671-4be0-9ff0-e3586d52e62c · outbound

This paper cites Communication-efficient federated learning for LEO constellations integrated with HAPs using hybrid NOMA-OFDM,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Communication-efficient federated learning for LEO constellations integrated with HAPs using hybrid NOMA-OFDM,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.878651Z

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Observation dfdb79d5-1e4c-4efa-ad66-a9fea08d3404 · outbound

This paper cites Energy-efficient resource manage- ment for federated learning in LEO satellite IoT,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Energy-efficient resource manage- ment for federated learning in LEO satellite IoT,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.869322Z

Source-reported events for the cited work

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Observation 0c36c86e-62ec-4c35-9f23-b27847892d22 · outbound

This paper cites Edge selection and clustering for federated learning in optical inter-LEO satellite constellation,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Edge selection and clustering for federated learning in optical inter-LEO satellite constellation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.859472Z

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Observation 54ce027d-7873-47fa-8592-368b0b536b25 · outbound

This paper cites A survey on satellite networks with federated learning to analyze data or manage resource,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks A survey on satellite networks with federated learning to analyze data or manage resource,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.850716Z

Source-reported events for the cited work

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Observation b6e83c3c-0a9b-4b72-8a23-4e3279bac35f · outbound

This paper cites Decomposition and meta-DRL based multi-objective optimization for asynchronous federated learning in 6G-satellite systems,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Decomposition and meta-DRL based multi-objective optimization for asynchronous federated learning in 6G-satellite systems,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.842184Z

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Observation 0b823a0e-4fb0-4339-af0d-b2c26465fa53 · outbound

This paper cites Cross-domain federated computation offloading for age of information minimization in satellite-airborne-terrestrial networks,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Cross-domain federated computation offloading for age of information minimization in satellite-airborne-terrestrial networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.833486Z

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Observation ca0fc5dc-4134-4481-b666-3e0beda54973 · outbound

This paper cites Exploitation maximization of unlabeled data for federated semi-supervised learning,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Exploitation maximization of unlabeled data for federated semi-supervised learning,

Reference 23

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Observation affda217-8a3a-49b5-accc-3487f7c22a94 · outbound

This paper cites Federated semi- supervised learning with inter-client consistency & disjoint learning,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Federated semi- supervised learning with inter-client consistency & disjoint learning,

Reference 24

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Observation 5bb26295-54eb-44ba-8794-414a26f35ac1 · outbound

This paper cites SemiFL: Semi-supervised federated learning for unlabeled clients with alternate training,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks SemiFL: Semi-supervised federated learning for unlabeled clients with alternate training,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.806898Z

Source-reported events for the cited work

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Observation 0fbfa5ef-6c95-4632-ae42-fc32808bdab9 · outbound

This paper cites Toward fast personalized semi-supervised federated learning in edge networks: Algorithm design and theoretical guarantee,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Toward fast personalized semi-supervised federated learning in edge networks: Algorithm design and theoretical guarantee,

Reference 26

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Observation 06f41e25-a8c8-4e36-bd9f-95c358e190af · outbound

This paper cites Boosting semi-supervised federated learning by effectively exploiting server-side knowledge and client-side unconfident samples,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Boosting semi-supervised federated learning by effectively exploiting server-side knowledge and client-side unconfident samples,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.788699Z

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Observation 235878b6-e9b1-4113-acbe-1a2a33e8133e · outbound

This paper cites Hybrid-FL for wireless networks: Cooperative learning mechanism us- ing non-IID data,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Hybrid-FL for wireless networks: Cooperative learning mechanism us- ing non-IID data,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.778590Z

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Observation 4290df84-a3bc-485c-8e5e-1449e9e71b66 · outbound

This paper cites Gradient scheduling with global momentum for asynchronous federated learning in edge environment,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Gradient scheduling with global momentum for asynchronous federated learning in edge environment,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.769561Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8e0dedff-89e6-4995-b382-b2ca3497a96a · outbound

This paper cites A triple-step asynchronous fed- erated learning mechanism for client activation, interaction optimization, and aggregation enhancement,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks A triple-step asynchronous fed- erated learning mechanism for client activation, interaction optimization, and aggregation enhancement,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.760903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 21fed80f-ab27-4018-ba47-8c341ea36deb · outbound

This paper cites Towards efficient asynchronous federated learning in heterogeneous edge environments,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Towards efficient asynchronous federated learning in heterogeneous edge environments,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.751739Z

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Observation 97b18c56-ff0b-4f25-9d1f-6bff9e049677 · outbound

This paper cites FixMatch: Simplifying semi- supervised learning with consistency and confidence,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks FixMatch: Simplifying semi- supervised learning with consistency and confidence,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.742654Z

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Observation 9e65afa2-0229-4ea1-8a06-e9367134c532 · outbound

This paper cites RandAugment: Practical automated data augmentation with a reduced search space,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks RandAugment: Practical automated data augmentation with a reduced search space,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.732999Z

Source-reported events for the cited work

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Observation 77989e50-df14-4a40-9431-b1592eea8fe4 · outbound

This paper cites AC-SGD: Adaptively compressed sgd for communication-efficient distributed learning,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks AC-SGD: Adaptively compressed sgd for communication-efficient distributed learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.723766Z

Source-reported events for the cited work

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Observation 7ff1beff-7564-4ee3-872d-da7d8f42d803 · outbound

This paper cites On the conver- gence of fedavg on non-iid data,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks On the conver- gence of fedavg on non-iid data,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.715078Z

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Observation f260e221-fa30-4c59-afa6-a5bd5290f9dd · outbound

This paper cites Deepsat: a learning framework for satellite imagery,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Deepsat: a learning framework for satellite imagery,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.706257Z

Source-reported events for the cited work

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Observation 0cf45394-dd9d-4210-bd4f-535cf81e4e86 · outbound

This paper cites Wide Residual Networks.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Wide Residual Networks

Reference 37

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Source-reported events for the cited work

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Observation e67f61cf-4c96-4e1f-94ce-40fcb06f201a · outbound

This paper cites QSGD: Communication-efficient SGD via gradient quantization and encoding,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks QSGD: Communication-efficient SGD via gradient quantization and encoding,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.697520Z

Source-reported events for the cited work

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Observation e0c50c37-fdf0-44d0-a9ca-ab7a134f596a · outbound

This paper cites Delay optimization for cooperative multi-tier computing in integrated satellite-terrestrial networks,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Delay optimization for cooperative multi-tier computing in integrated satellite-terrestrial networks,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T11:56:02.532936Z

Source-reported events for the cited work

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Observation 4c29ed60-1811-43e7-9758-7504692a1d2c · outbound

This paper cites Satellite edge computing with collaborative computation offloading: An intelligent deep determin- istic policy gradient approach,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Satellite edge computing with collaborative computation offloading: An intelligent deep determin- istic policy gradient approach,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.683224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 37369517-7c81-4a67-a997-c9fde2892ec2 · outbound

This paper cites Client-edge-cloud hier- archical federated learning,.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks Client-edge-cloud hier- archical federated learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.673603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 06441021-fb56-4949-9992-d958c4d3a01b · outbound

This paper cites A distinctive feature of his research is its real- world impact and industry focus.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks A distinctive feature of his research is its real- world impact and industry focus

Reference 2006

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.653819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b7d89aa1-ec8b-4922-a3eb-4164259a24a3 · outbound

This paper cites His research interests encompass col- laborative learning/optimization, edge intelligence, graph learning, and the Internet of Things.

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks His research interests encompass col- laborative learning/optimization, edge intelligence, graph learning, and the Internet of Things

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:56:02.663790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 3641e118-894a-492e-bb49-f385f70d0f59 · inbound

FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations cites this paper.

FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-05T19:13:02.232357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.