Paper Citation Record · LEDGER

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2412.17081.

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

pith.paper-citation-record.v1
2412.17081 v1

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Pith citing papers itemized under the disclosed page cap.

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41 of 41 outbound references displayed

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Outbound references

Observation 0615e26e-3e08-42cf-aca8-22a39b872d8c · outbound

This paper cites Empow- ering hwns with efficient data labeling: A clustered federated semi- supervised learning approach,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Empow- ering hwns with efficient data labeling: A clustered federated semi- supervised learning approach,

Reference 1

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Observation c5c8db49-a090-48db-be16-52336e9fc2eb · outbound

This paper cites Sensor- based activity recognition,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Sensor- based activity recognition,

Reference 2

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This paper cites Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review,

Reference 3

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This paper cites Deep learning for computer vision: A brief review,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Deep learning for computer vision: A brief review,

Reference 4

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This paper cites A survey on vision transformer,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning A survey on vision transformer,

Reference 5

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Observation dcbe3f1a-bd7e-4de7-a13d-dcfeb6f6a71d · outbound

This paper cites Application of machine learning in wireless networks: Key techniques and open issues,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Application of machine learning in wireless networks: Key techniques and open issues,

Reference 6

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Observation 8fcfd034-fb84-440e-9e33-227b1471ffa4 · outbound

This paper cites From federated to fog learning: Distributed machine learning over heterogeneous wireless networks,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning From federated to fog learning: Distributed machine learning over heterogeneous wireless networks,

Reference 7

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Observation 7190c758-b9ae-48b7-9a04-9dd8d6dc273c · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Federated Learning: Strategies for Improving Communication Efficiency

Reference 8

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This paper cites Hierarchical federated learning across heterogeneous cellular networks,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Hierarchical federated learning across heterogeneous cellular networks,

Reference 9

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Observation cb7ac9fa-c3a2-4e0b-8848-68ea75220b01 · outbound

This paper cites Federated learning over wireless iot networks with optimized communication and resources,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Federated learning over wireless iot networks with optimized communication and resources,

Reference 10

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Observation ee17f9f9-9b88-4c93-af0e-65168b091859 · outbound

This paper cites Fine-grained data selection for improved energy efficiency of federated edge learning,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Fine-grained data selection for improved energy efficiency of federated edge learning,

Reference 11

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This paper cites Hfel: Joint edge asso- ciation and resource allocation for cost-efficient hierarchical federated edge learning,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Hfel: Joint edge asso- ciation and resource allocation for cost-efficient hierarchical federated edge learning,

Reference 12

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This paper cites Federated learning for healthcare informatics,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Federated learning for healthcare informatics,

Reference 13

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Observation 42b3f9d4-ea25-475a-8a12-a4abc6ed8306 · outbound

This paper cites Federated learning with soft cluster- ing,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Federated learning with soft cluster- ing,

Reference 14

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Observation 0dbc0dd4-452b-4769-90d2-8f1e1739aa0b · outbound

This paper cites Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,

Reference 15

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Observation 62d806e6-5166-407d-833f-dbe4a8af5e7c · outbound

This paper cites Three Approaches for Personalization with Applications to Federated Learning.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Three Approaches for Personalization with Applications to Federated Learning

Reference 16

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This paper cites An efficient frame- work for clustered federated learning,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning An efficient frame- work for clustered federated learning,

Reference 17

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This paper cites Client selection approach in support of clustered federated learning over wireless edge networks,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Client selection approach in support of clustered federated learning over wireless edge networks,

Reference 18

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Energy-efficient clustering to address data heterogeneity in federated learning,

Reference 19

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Observation 524eb2d5-15e0-4a7f-90cc-bbe0a9067ced · outbound

This paper cites On the Convergence of Clustered Federated Learning.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning On the Convergence of Clustered Federated Learning

Reference 20

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Dynamic clustering in federated learning,

Reference 21

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Adaptive client clustering for efficient federated learning over non-iid and imbalanced data,

Reference 22

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Intelligent model aggregation in hierarchical clustered federated mul- titask learning,

Reference 23

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Active client selection for clustered federated learning,

Reference 24

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Fair selection of edge nodes to participate in clustered federated multitask learning,

Reference 25

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Clustered and multi-tasked federated distillation for heterogeneous and resource constrained industrial iot applications,

Reference 26

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This paper cites Semi-supervised federated learning over heterogeneous wireless iot edge networks: Framework and algorithms,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Semi-supervised federated learning over heterogeneous wireless iot edge networks: Framework and algorithms,

Reference 27

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Semifl: Semi-supervised federated learning for unlabeled clients with alternate training,

Reference 28

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Semi-supervised and person- alized federated activity recognition based on active learning and label propagation,

Reference 29

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Observation d4d20e4f-3eb9-46dd-aa9f-1fbd004e3853 · outbound

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

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Towards fast personalized semi-supervised federated learning in edge networks: Algorithm design and theoretical guarantee,

Reference 30

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This paper cites Semipfl: person- alized semi-supervised federated learning framework for edge intelli- gence,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Semipfl: person- alized semi-supervised federated learning framework for edge intelli- gence,

Reference 31

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Adaptive hier- archical federated learning over wireless networks,

Reference 32

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Hierarchical federated learning with quantization: Convergence analysis and system design,

Reference 33

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Auction-based cluster federated learning in mobile edge computing systems,

Reference 34

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Flexible clustered federated learning for client-level data distribution shift,

Reference 35

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This paper cites Edge devices clustering for federated visual classification: A feature norm based framework,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Edge devices clustering for federated visual classification: A feature norm based framework,

Reference 36

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

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Communication-efficient learning of deep networks from decentralized data,

Reference 37

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This paper cites Meta-gating framework for fast and continuous resource optimization in dynamic wireless environments,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Meta-gating framework for fast and continuous resource optimization in dynamic wireless environments,

Reference 38

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This paper cites Convergence analysis of a distributed optimization algorithm with a general unbalanced directed communica- tion network,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Convergence analysis of a distributed optimization algorithm with a general unbalanced directed communica- tion network,

Reference 39

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This paper cites Improving robustness using generated data,.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning Improving robustness using generated data,

Reference 40

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Observation 9b6dbe24-251c-4c71-888d-d10602544d6f · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning LEAF: A Benchmark for Federated Settings

Reference 41

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Pith citing papers

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