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Smart city article details
| Title | Federated Trustworthy Ai Architecture For Smart Cities |
|---|---|
| ID_Doc | 26386 |
| Authors | Utomo S.; John A.; Rouniyar A.; Hsu H.-C.; Hsiung P.-A. |
| Year | 2022 |
| Published | ISC2 2022 - 8th IEEE International Smart Cities Conference |
| DOI | http://dx.doi.org/10.1109/ISC255366.2022.9922069 |
| Abstract | As the number of smart citizens or users grows, several governments are eager to develop smart cities. All stakeholders, including intelligent users, demand an AI system that is trustworthy. To be considered trustworthy, an AI system must meet seven key criteria: (1) human agency and oversight, (2) robustness and safety, (3) privacy and data governance, (4) transparency, (5) diversity, nondiscrimination, and fairness, (6) societal and environmental well-being, and (7) accountability. By merging the existing trustworthy AI framework from KServe and Federated Learning with a more reliable model aggregation protocol than earlier studies, we introduced the federated trustworthy Artificial Intelligence (FTAI) architecture. With the integration of FedPSO and AIF360, we updated the FedCS technique. The proposed architecture meets all seven of the essential requirements to a high degree of satisfaction. This paper also includes a detailed explanation of this claim. The scenario demonstrates that the new global model comes with clear measures of fairness metrics, ensuring that the model is devoid of bias. © 2022 IEEE. |
| Author Keywords | federated learning; smart city; trustworthy AI |
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