Zekai Li1, Wenfeng Wang2,3,4,5,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2881-2899, 2023, DOI:10.32604/cmes.2023.028670
Abstract We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge— to
learn damaged objects in aerial videos. A meta-learning system was integrated with the fuzzy broad learning system
to further develop the theory of federated learning. Both the mixed picture set of aerial video segmentation and
the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning
system. The study results indicated that the object classification accuracy is up to 90% and the average time cost in
damage detection is only 0.277 s. Consequently, the broad federated meta-learning system is efficient and… More >
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