GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS | Paper Citation Record

Observation 9b44de6f-3f32-4d70-8585-b3756991ea7a · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Deep reinforce- ment learning for solving the trip planning query,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.229395Z

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Observation b5e16831-c326-4f53-84a7-7eec68581bc3 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Adaptive segmentation enhanced asynchronous federated learning for sustainable intelligent transportation systems,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.215532Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS An integrated medical rec- ommendation mechanism combining promote product singular value decomposition and knowledge graph,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.200347Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS C2lrec: Causal contrastive learning for user cold-start rec- ommendation with social variable,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.184312Z

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This paper cites Differential evolution with joint adaptation of mutation strategies and control parameters via distributed proximal policy optimization,.

GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Differential evolution with joint adaptation of mutation strategies and control parameters via distributed proximal policy optimization,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.169393Z

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Observation 2e910cbe-f7e5-47c7-bb75-78c850a35ff2 · outbound

This paper cites Differential privacy in edge computing-based smart city applications: Security issues, solutions and future directions,.

GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Differential privacy in edge computing-based smart city applications: Security issues, solutions and future directions,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.155740Z

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Observation 9316db32-937b-4f37-95eb-538cf64ae061 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Spatial–temporal federated transfer learning with multi-sensor data fusion for cooperative positioning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.140025Z

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Observation bff22eac-98f8-4568-824d-dc8e559857bb · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Xrl-shap-cache: an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.126090Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Decentralized federated graph learning with lightweight zero trust architecture for next-generation networking security,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.110647Z

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Observation 2ded9abf-d417-42fb-a0f7-c34a6313d105 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Cyber attack detection in iot networks with small samples: Implementation and analysis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.095025Z

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Observation e977a3a8-b61c-467d-8a40-26919dfdb850 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Digital twin enhanced federated reinforcement learning with lightweight knowledge distillation in mobile networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.080832Z

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Observation eff52e15-ecc7-445c-96b8-41b9f924bf27 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Research on medical image classification based on improved fedavg algorithm,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.065903Z

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Observation 6e274d44-23c5-476e-87c5-111c63356b74 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Federated anomaly detection with isolation forest for iot network traffics,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.050518Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Machine learning models that remember too much,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.036510Z

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Observation c429f171-e8ac-4ceb-a286-da907fa28560 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Pairing based anonymous and secure key agreement protocol for smart grid edge computing infrastructure,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.020088Z

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Observation 0ee8bb78-80cb-4db8-8a6d-88f234dfa5fc · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Information theoretic learning-enhanced dual-generative adversarial networks with causal representation for robust ood general- ization,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:01.004984Z

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Observation b99d11d6-98e4-42a1-9758-66c200dde388 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Exploiting unintended feature leakage in collaborative learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.990795Z

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Observation cb66644d-6fa9-4682-8a42-5515d7d0e352 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Differentially private system for residential energy management via markov decision process,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.976689Z

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Observation b6e3628c-fdc0-49df-892e-379017e36e65 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Adap dp-fl: Differentially private federated learning with adaptive noise,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.961960Z

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Observation 138aa07a-cfb8-4023-8d09-9c5c9a6eb973 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Reconstructed graph neural network with knowledge distillation for lightweight anomaly detection,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.948022Z

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Observation be2ca33a-dc4c-45be-a724-022f5dc0a805 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Hierarchical federated learning with social context clustering-based participant selection for internet of medical things applications,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.934086Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS A systematic review of homomorphic encryption and its contributions in healthcare industry,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.919709Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Communication-efficient learning of deep networks from decentralized data,

Reference 23

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Federated optimization in heterogeneous networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.885063Z

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Observation 6bfc8757-0bf0-40e6-9e4b-a6deff1cd9cb · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Scaffold: Stochastic controlled averaging for federated learn- ing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.870190Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Federated learning-based anomaly detection with isolation forest in the iot-edge continuum,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.855337Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Blockchain- enabled secure, fair and scalable data sharing in zero-trust edge-end environment,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.839482Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Fedexp: Speeding up federated averaging via extrapolation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.822911Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Model inversion attacks that exploit confidence information and basic countermeasures,

Reference 29

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Observation ccf79866-e069-4510-88c5-79d4bf502d85 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Inverting gra- dients—how easy is it to break privacy in federated learning?

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.797912Z

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 b70118ec-d1cc-4860-b5af-0c3525fe65ed · outbound

This paper cites Differential privacy,.

GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Differential privacy,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.781499Z

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 fd22b574-2a65-4a76-8e18-9b8d4966a14b · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Data release for machine learning via correlated differential privacy,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.764963Z

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Gaussian differential privacy,

Reference 33

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Observation 063c5df3-1d67-4929-bde7-d5bb9ca35bcf · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS R ´enyi differential privacy,

Reference 34

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Source-reported events for the cited work

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Practicing differential privacy in health care: A review,

Reference 35

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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 fcc3294d-3041-42a4-855e-b8d5d2204286 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Comparative analysis of local differential privacy schemes in healthcare datasets,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.705850Z

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Observation 9aea0de2-3c55-49f2-bddc-c71617830dc1 · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Dpsur: Accelerating differentially private stochastic gradient descent using selective update and release,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.689915Z

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Observation 55177687-a7ef-4cef-8ea0-70f30d8c5b9e · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Sa-dpsgd: Differentially private stochastic gradient descent based on simulated annealing,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.672020Z

Source-reported events for the cited work

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Observation 5fa55c6d-300c-4f7d-9841-42007b51447c · outbound

This paper cites Personalized federated learning with model-contrastive learning for multi-modal user modeling in human-centric metaverse,.

GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Personalized federated learning with model-contrastive learning for multi-modal user modeling in human-centric metaverse,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.657040Z

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 d845fdbb-bd0c-41fa-b673-2d8401646fbe · outbound

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GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Federated distillation and blockchain empowered secure knowledge sharing for internet of medical things,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.641396Z

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 c2e324be-545b-4598-a8b6-23ec6846f504 · outbound

This paper cites The algorithmic foundations of differential privacy,.

GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS The algorithmic foundations of differential privacy,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.627633Z

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 0aef441d-353d-40e4-a17e-5dec425168b0 · outbound

This paper cites Higher order fractal belief r´enyi divergence with its applications in pattern classification,.

GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Higher order fractal belief r´enyi divergence with its applications in pattern classification,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.613348Z

Source-reported events for the cited work

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Observation 06d312a7-55ef-467a-aa73-2b627fd77bba · outbound

This paper cites Hypothesis testing interpretations and r ´enyi differential privacy,.

GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS Hypothesis testing interpretations and r ´enyi differential privacy,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.599993Z

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 e4625db7-ec45-49a0-8277-aee772c6ef53 · outbound

This paper cites The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation,.

GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:04:00.585753Z

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.

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