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Spectrum Occupancy Detection Supported by Federated Learning

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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{L}ukasz Ku{l}acz

Networking and Internet Architecture · Submitted 6 Mar 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The proposed algorithm is effective, especially in the context of a set of sensors with faulty sensors.

how it is stated · asserted 1.00 read the record →

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arXiv ID2403.03617 (version 1)
Pith IDA333GFHD, Pith's permanent record of this paper and its review
Citation{\L}ukasz Ku{\l}acz. "Spectrum Occupancy Detection Supported by Federated Learning." arXiv:2403.03617 (2024). https://pith.science/paper/A333GFHD
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,704 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.27 · human participants 0.03 · controlled comparison 0.11 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.11 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Dynamic spectrum access is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is effective spectrum occupancy detection. In many cases, machine learning algorithms improve detection effectiveness. Because of the recent trend of using federated learning, a federated learning algorithm is presented in the context of distributed spectrum occupancy detection. The results of the work presented in the paper are based on actual signal samples collected in the laboratory. The proposed algorithm is effective, especially in the context of a set of sensors with faulty sensors.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Dynamic spectrum access is essential for radiocommunication and its limited spectrum resources. background 1.00 0.04 0.02 asserted 1.00
s1 The key element of dynamic spectrum access systems is effective spectrum occupancy detection. background 1.00 0.06 0.04 asserted 1.00
s2 In many cases, machine learning algorithms improve detection effectiveness. background 1.00 0.07 0.04 asserted 0.71
s3 Because of the recent trend of using federated learning, a federated learning algorithm is presented in the context of distributed spectrum occupancy detection. contribution 0.93 0.86 0.08 asserted 0.99
s4 The results of the work presented in the paper are based on actual signal samples collected in the laboratory. method 1.00 0.94 0.03 asserted 1.00
s5 The proposed algorithm is effective, especially in the context of a set of sensors with faulty sensors. result 0.96 0.90 0.73 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from observation or experiment 0.99.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Dynamic spectrum access is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is effective spectrum occupancy detection. In many cases, machine learning algorithms improve detection effectiveness. Because of the recent trend of using federated learning, a federated learning algorithm is presented in the context of distributed spectrum occupancy detection. The results of the work presented in the paper are based on actual signal samples collected in the laboratory. The proposed algorithm is effective, especially in the context of a set of sensors with faulty sensors.

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