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User Scheduling in Federated Learning over Energy Harvesting Wireless Networks.

, , , , and . GLOBECOM, page 1-6. IEEE, (2021)

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Deep-Gap: A Deep Learning Framework for Forecasting Crowdsourcing Supply-Demand Gap Based on Imaging Time Series and Residual Learning., and . CloudCom, page 279-286. IEEE, (2019)User Scheduling in Federated Learning over Energy Harvesting Wireless Networks., , , , and . GLOBECOM, page 1-6. IEEE, (2021)Gravitational weighted fuzzy c-means with application on multispectral image segmentation., , and . IPTA, page 92-96. IEEE, (2014)Multimodal Deep Learning Approach for Joint EEG-EMG Data Compression and Classification., , , , and . WCNC, page 1-6. IEEE, (2017)Mobile Crowdsourced Sensors Selection for Journey Services., , , and . ICSOC, volume 11236 of Lecture Notes in Computer Science, page 463-477. Springer, (2018)Federated Learning over Energy Harvesting Wireless Networks., , , , and . CoRR, (2021)Cluster validity index based on n-sphere., , and . AICCSA, page 144-147. IEEE Computer Society, (2014)Edge guided total variation for image denoising., , , and . CISS, page 1-6. IEEE, (2017)Multiview topological data analysis for crowdsourced service supply-demand gap prediction., and . IWCMC, page 1818-1823. IEEE, (2020)A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems., , , , , , , and . IEEE Access, (2018)