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CDFed: Contribution-based Dynamic Federated Learning for Managing System and Statistical Heterogeneity.

, , , , and . NOMS, page 1-5. IEEE, (2023)

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Integrated Sensing, Localization, and Communication in Holographic MIMO-Enabled Wireless Network: A Deep Learning Approach., , , , , and . IEEE Trans. Netw. Serv. Manag., 21 (1): 789-809 (February 2024)CDFed: Contribution-based Dynamic Federated Learning for Managing System and Statistical Heterogeneity., , , , and . NOMS, page 1-5. IEEE, (2023)Knowledge Distillation in Federated Learning: Where and How to Distill?, , , , , and . APNOMS, page 18-23. IEEE, (2023)Artificial Intelligence Framework for Target Oriented Integrated Sensing and Communication in Holographic MIMO., , , , and . NOMS, page 1-7. IEEE, (2023)MP-FedCL: Multi-Prototype Federated Contrastive Learning for Edge Intelligence., , , , , , and . CoRR, (2023)Impact learning: A learning method from feature's impact and competition., , , , , , , and . J. Comput. Sci., (May 2023)Impact Learning: A Learning Method from Features Impact and Competition., , , , , , , and . CoRR, (2022)An Artificial Intelligent-Driven Semantic Communication Framework for Connected Autonomous Vehicular Network., , , , , and . ICOIN, page 352-357. IEEE, (2023)Performance Analysis of Transmission Rate and Decoding Rate Using Minimum Energy Channel Codes for Nanoscale Wireless Communication., , , , and . Wirel. Pers. Commun., 118 (2): 1343-1364 (2021)Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection., , , , , , , , and . Hum. Centric Intell. Syst., 3 (4): 441-460 (December 2023)