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Paper Citation Record · LEDGER

Energy-Efficient Federated Learning for AIoT using Clustering Methods

As of 20 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2505.09704.

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

pith.paper-citation-record.v1
2505.09704 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:32:44.863453Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation 547e373d-f2f9-4457-87b1-8d48af17bd71 · outbound

This paper cites Trends in data centre energy consumption under the european code of conduct for data centre energy efficiency,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Trends in data centre energy consumption under the european code of conduct for data centre energy efficiency,

Reference 1

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Observation 1a21c94b-b687-4152-909e-6557c495f58b · outbound

This paper cites Green cloud computing: Balancing energy in processing, storage, and transport,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Green cloud computing: Balancing energy in processing, storage, and transport,

Reference 2

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Observation 12c639ae-40b7-4b61-9abf-3aeb98e7cc57 · outbound

This paper cites Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures,

Reference 3

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Observation f55fa8a7-bb85-467c-90fb-f2f5ecc0501b · outbound

This paper cites The Cost of Training Machine Learning Models over Distributed Data Sources,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods The Cost of Training Machine Learning Models over Distributed Data Sources,

Reference 4

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Observation f861f3ba-a828-4d5a-b7c7-392dc59b1806 · outbound

This paper cites Federated Learning With Co- operating Devices: A Consensus Approach for Massive IoT Networks,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Federated Learning With Co- operating Devices: A Consensus Approach for Massive IoT Networks,

Reference 5

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Observation 1c38cbb0-1491-4b0c-aa3a-c5f7e2436912 · outbound

This paper cites Learning- Based Computation Offloading for IoT Devices With Energy Harvest- ing,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Learning- Based Computation Offloading for IoT Devices With Energy Harvest- ing,

Reference 6

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Observation 703a9193-e647-47a0-9058-ef97c60aab7c · outbound

This paper cites Energy-Efficient Artificial Intelligence of Things With Intelligent Edge,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Energy-Efficient Artificial Intelligence of Things With Intelligent Edge,

Reference 7

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Observation 1169a79b-f727-4fd0-88e7-c48ee18e87c0 · outbound

This paper cites A Survey on Federated Learning for Resource-Constrained IoT Devices,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods A Survey on Federated Learning for Resource-Constrained IoT Devices,

Reference 8

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Observation 2d9e42ed-3af8-44ed-ada9-9c49d0e77ee9 · outbound

This paper cites A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond,

Reference 9

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Observation 8b1dce76-c050-423f-a6d8-4182ff8f31a7 · outbound

This paper cites Towards Federated Learning at Scale: System Design,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Towards Federated Learning at Scale: System Design,

Reference 10

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Observation f0ae38c6-0e85-4216-bbf6-c452934cc0ff · outbound

This paper cites On the Convergence of FedAvg on Non-IID Data,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods On the Convergence of FedAvg on Non-IID Data,

Reference 11

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Observation 8c59b98c-a50e-440e-a1e7-30234bcf250e · outbound

This paper cites Federated Learning With Non-IID Data: A Survey,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Federated Learning With Non-IID Data: A Survey,

Reference 12

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Observation ff935fed-ff14-4f1a-a941-57e8af35e7c8 · outbound

This paper cites Federated Optimization in Heterogeneous Networks,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Federated Optimization in Heterogeneous Networks,

Reference 13

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Observation f3f55d12-6b55-4100-8522-f0c474a17694 · outbound

This paper cites Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies

Reference 14

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Observation 5906e0c9-8d29-43aa-80dc-f74354297693 · outbound

This paper cites Optimal Client Sampling for Federated Learning.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Optimal Client Sampling for Federated Learning

Reference 15

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Observation 47d4836b-06cf-479d-be29-8e184a22c3a3 · outbound

This paper cites FedCor: Correlation-Based Active Client Selection Strategy for Het- erogeneous Federated Learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods FedCor: Correlation-Based Active Client Selection Strategy for Het- erogeneous Federated Learning,

Reference 16

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Observation bafd248d-468a-4272-9db2-015d02252da5 · outbound

This paper cites Delta: Diverse client sam- pling for fasting federated learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Delta: Diverse client sam- pling for fasting federated learning,

Reference 17

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Observation b7306199-a003-4c89-b7d7-c02d16b62172 · outbound

This paper cites Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning,

Reference 18

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Observation 1b053b45-f1c4-4f9b-8924-9c354ea329a8 · outbound

This paper cites Diverse client selection for federated learning via submodular max- imization,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Diverse client selection for federated learning via submodular max- imization,

Reference 19

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Observation c483594b-4d48-4333-9bc5-1b2e261497fc · outbound

This paper cites Fast heterogeneous federated learning with hybrid client selection,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Fast heterogeneous federated learning with hybrid client selection,

Reference 20

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Observation fae85d4c-2738-42e5-bf5e-076a85799aff · outbound

This paper cites FLIS: Clustered Fed- erated Learning Via Inference Similarity for Non-IID Data Distribution ,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods FLIS: Clustered Fed- erated Learning Via Inference Similarity for Non-IID Data Distribution ,

Reference 21

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Observation 035adfaf-ce28-48fc-ab9f-02e0cc07468f · outbound

This paper cites No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non- IID Data,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non- IID Data,

Reference 22

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Observation 44e67a8e-cf52-4522-a546-c13124ccbe59 · outbound

This paper cites Fedcorr: Multi- stage federated learning for label noise correction,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Fedcorr: Multi- stage federated learning for label noise correction,

Reference 23

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Observation 19697463-d50b-4cfe-8f1c-422c3890d3b0 · outbound

This paper cites Learn from Others and Be Yourself in Heterogeneous Federated Learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Learn from Others and Be Yourself in Heterogeneous Federated Learning,

Reference 24

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Observation 38425559-51ee-4d07-876d-cd4a6067a2fd · outbound

This paper cites CIFAR-10 (Canadian Institute for Advanced Research).

Energy-Efficient Federated Learning for AIoT using Clustering Methods CIFAR-10 (Canadian Institute for Advanced Research)

Reference 25

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Observation 9cc2439a-c7c8-4179-9945-318c036ebb5a · outbound

This paper cites Energy Efficient Federated Learning Over Wireless Communication Networks,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Energy Efficient Federated Learning Over Wireless Communication Networks,

Reference 26

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Observation cd4f0301-e936-4d8b-9ab7-ea9916ac3ded · outbound

This paper cites Fedgroup-prune: Iot device amicable and training-efficient federated learning via combined group lasso sparse model pruning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Fedgroup-prune: Iot device amicable and training-efficient federated learning via combined group lasso sparse model pruning,

Reference 27

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Observation 4e794803-0203-41b3-b7bd-b8d0859ca91e · outbound

This paper cites Feder- ated Learning Under Heterogeneous and Correlated Client Availability,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Feder- ated Learning Under Heterogeneous and Correlated Client Availability,

Reference 28

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Observation 2af37693-1547-4824-9838-66bcd28e83c9 · outbound

This paper cites Measuring Data Similarity for Efficient Federated Learning: A Feasibility Study ,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Measuring Data Similarity for Efficient Federated Learning: A Feasibility Study ,

Reference 29

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Observation 47b1cd74-0599-4e37-a2ef-15c807aa0328 · outbound

This paper cites Advances and open problems in federated learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Advances and open problems in federated learning,

Reference 30

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Observation 6c4655c0-4a4a-4b75-aa57-024336e910f9 · outbound

This paper cites Federated Learning on Non-IID Data Silos: An Experimental Study,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Federated Learning on Non-IID Data Silos: An Experimental Study,

Reference 31

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Observation b62871c8-4869-4563-b976-ff3c8b2f5229 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 32

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Observation aa76aaf4-a6c7-40c2-8ac2-4b5bc0240245 · outbound

This paper cites Communication-Efficient Distributionally Robust Decentralized Learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Communication-Efficient Distributionally Robust Decentralized Learning,

Reference 33

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Observation 0c277c51-c3d3-4d7d-a52f-ab628345266e · outbound

This paper cites A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks,

Reference 34

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Observation 095c3655-847d-4edb-8321-b82a637bb96b · outbound

This paper cites Local SGD Converges Fast and Communicates Little,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Local SGD Converges Fast and Communicates Little,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.284224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.775929Z digest=sha256:95e8f585754a6eefd2f3d5beedf22c52b60e0b5300e25d590ab5419fd3f974a7

Observation 44be7339-6a70-4c06-bad3-1736e8e503fb · outbound

This paper cites Convergence time optimiza- tion for federated learning over wireless networks,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Convergence time optimiza- tion for federated learning over wireless networks,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.272729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.779712Z digest=sha256:301c786d414faae97daac52d1a9ed6ba51771f64d6ee002a00cca425b2b4717b

Observation 8bbad7bd-cdbc-471f-8a62-a3cad3dc8d5e · outbound

This paper cites Optimal Client Sampling for Federated Learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Optimal Client Sampling for Federated Learning,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.261204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.783598Z digest=sha256:dc939650cad41f2034d3e4fd4482cd2946b5bda761d078b85183c775f7799548

Observation 59378d92-0ad8-4f95-bd8e-50863df4f4b1 · outbound

This paper cites Neyman, On the Two Different Aspects of the Representative Method: the Method of Stratified Sampling and the Method of Purposive Selec- tion.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Neyman, On the Two Different Aspects of the Representative Method: the Method of Stratified Sampling and the Method of Purposive Selec- tion

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.248247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.787382Z digest=sha256:8b68b487da41150021f61baa45d6b761258fa9a662b87af3a0568873444fa341

Observation 690225b1-14a4-4cb3-a0d8-b43085356b42 · outbound

This paper cites Repulsive clustering based pilot assignment for cell-free massive MIMO systems,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Repulsive clustering based pilot assignment for cell-free massive MIMO systems,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.233705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.791113Z digest=sha256:b9f97f44025c4f75d4710254cc162ba5db3e8f2a49fee2ac336db2d520a4585d

Observation ee27d44f-14da-41c5-a2eb-52b8aebacb23 · outbound

This paper cites Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition ,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition ,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.218304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.794813Z digest=sha256:0dab622723b4606617da1c951760988c1f426f5f700666873954063918d789cd

Observation c1a3d49e-1604-4dd0-b187-47d2fdd78a8b · outbound

This paper cites Heuristic and special case algorithms for dispersion problems,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Heuristic and special case algorithms for dispersion problems,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.203948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.798555Z digest=sha256:f30106b615f9dad1d7f6dcba7fc6210a7999b9feb8305a30fd884e9b73e4971b

Observation 89af0f79-a229-4dfc-a80a-5729452bfdbf · outbound

This paper cites Inferring class-label distribution in feder- ated learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Inferring class-label distribution in feder- ated learning,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.191020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.801949Z digest=sha256:5338b6e3ec48853b48afbc710929f52e57b4a7e80f5e2f403a887568a468ffba

Observation 225d9b0f-4681-45b6-bb1d-d37e7bf6f6d8 · outbound

This paper cites Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.177505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.806153Z digest=sha256:7ddfa33e17a037c2249458f5808cdafa80da43d84eee67f0cc1a67a5e4d9d8a7

Observation 2b4686ba-71ef-471f-a219-54441fcd2fe5 · outbound

This paper cites mlco2/codecarbon: v2.4.1,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods mlco2/codecarbon: v2.4.1,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.163109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.813465Z digest=sha256:d132e8398da7df4e2f74b203054925ea41fe5ea5e6d3a312a0b2b307c8395ea9

Observation 49665268-719d-4276-98cf-bc9910e34598 · outbound

This paper cites Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.149417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.824324Z digest=sha256:59788a10b10712d60a71aab6c9bbef4a4e9bb2dd25638c22726e483e1d4ac310

Observation 7e4ac5ab-01a5-4b93-8041-c9fd89b37dfa · outbound

This paper cites How to estimate carbon footprint when training deep learning models? A guide and review,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods How to estimate carbon footprint when training deep learning models? A guide and review,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.137007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.830624Z digest=sha256:be35755ccfc3bdb4af48f819c3638f5fe392ca62581fc0a396e9e113ce1cc8aa

Observation c0cffb1b-5309-4bf4-91a1-107e55d6d861 · outbound

This paper cites Evaluating the RAM energy consumption at the stage of software development,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Evaluating the RAM energy consumption at the stage of software development,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.122640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.835813Z digest=sha256:9f3075ad7d97e1596a213515679343092e8107fbe91f1df6036a12fa6773e78a

Observation a2468431-55d9-4bfc-9ec4-f8748f8d820a · outbound

This paper cites IEEE 802.11 ax: High-efficiency WLANs,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods IEEE 802.11 ax: High-efficiency WLANs,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.103903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.844321Z digest=sha256:cd82f142777a85d45ac495f169ce28d908f438dea33384d337c13c993cfb1e2d

Observation d2b7a9f0-ee82-4e5a-9837-d7f7f5fd5eb9 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 49

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unresolved
no resolver link, observed 2026-08-15T21:32:44.848404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:32:44.848404Z digest=sha256:6ebf0171be2a4e352d4350c1d5fc173ed967c4293bb82eee7cdead98ae0f47ee

Observation e4da9b61-9122-443f-b705-4afe35f3d924 · outbound

This paper cites Differential privacy,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Differential privacy,

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-15T21:32:44.852157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:32:44.852157Z digest=sha256:0db2abebfdffda5995b937f5bd9b458d2e80f8336064f2ad9dce81af043be125

Observation 4e1c7d0c-e60b-4fd9-af9b-249ee58c804d · outbound

This paper cites The algorithmic foundations of differential privacy,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods The algorithmic foundations of differential privacy,

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-15T21:32:44.856084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:32:44.856084Z digest=sha256:eba9f698e852492f59e5c0832bdeefa37439c1049b9c41a787a8c6ac08941093

Observation a1c93bc4-298f-4389-b8a6-16c23f9b46af · outbound

This paper cites Federated learning with differential privacy: Algorithms and performance analysis,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Federated learning with differential privacy: Algorithms and performance analysis,

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-15T21:32:44.859781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:32:44.859781Z digest=sha256:0e75d9ee022e953bc1dbd4ff0aeeb0120aa38a80960860f295c777cae4e780fd

Observation 609ed780-528c-4c11-84a1-9241b8c1e564 · outbound

This paper cites Federated learning with differential privacy: Algorithms and performance analysis,.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Federated learning with differential privacy: Algorithms and performance analysis,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:32:45.057559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:32:44.863453Z digest=sha256:010c9523b7390a4f3a82cb4e6cfcfc54e435ebc4730562c5f9063a8b6e03d9d8

Observation a773aaa2-b677-4ec7-93d2-a42c4c0cc596 · outbound

This paper cites Available: https://doi.org/10.5281/zenodo.11171501.

Energy-Efficient Federated Learning for AIoT using Clustering Methods Available: https://doi.org/10.5281/zenodo.11171501

Reference 2024

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unresolved
no resolver link, observed 2026-08-15T21:32:44.819578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:32:44.819578Z digest=sha256:2e23edf512fc88c5b41700d501afd85c0cdd5487daeddd09435395cc2b122c3e

Pith citing papers

No inbound Pith citation observations are available.